Data driven natural language event detection and classification

ABSTRACT

Systems and processes for operating a digital assistant are provided. In accordance with one or more examples, a method includes, at a user device with one or more processors and memory, receiving unstructured natural language information from at least one user. The method also includes, in response to receiving the unstructured natural language information, determining whether event information is present in the unstructured natural language information. The method further includes, in accordance with a determination that event information is present within the unstructured natural language information, determining whether an agreement on an event is present in the unstructured natural language information. The method further includes, in accordance with a determination that an agreement on an event is present, determining an event type of the event and providing an event description based on the event type.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 62/348,898, filed on Jun. 11, 2016, entitled “Data DrivenNatural Language Event Detection and Classification,” which is herebyincorporated by reference in its entirety for all purposes.

FIELD

The present disclosure relates generally to a natural languageprocessing and, more specifically, to detecting and classifying eventsusing unstructured natural language information.

BACKGROUND

Event information may be included in unstructured natural languageinformation. For example, two users may use text messages to arrange alunch event. Once the users agree on the event, they may wish to createa calendar entry for the event. Conventionally, to create a calendarentry, a user is required to manually transfer event information fromunstructured natural language such as a text message to a calendarentry. Manually transferring event information typically requiresmultiple copy and paste operations or tedious manual entry. The processof manually creating the calendar entry is thus cumbersome and timeconsuming. Accordingly, there is a need for more efficient methods fordetecting and classifying events based on unstructured natural languageinformation.

BRIEF SUMMARY

Some techniques for detecting events use structured messages in HTMLformat (e.g., computer-generated emails from airlines, hotels, or travelservices). Since the messages are structured, detection is relativelystraightforward. Detecting and classifying events within unstructurednatural language information is more challenging due to inherentambiguity of the natural language. Unstructured natural languageinformation includes, for example, user generated text messages,electronic mails, voice mails, instant messages, conversations, or thelike. Unstructured natural language is widely used and thereforedetecting and classifying events based on the unstructured naturallanguage information is important and desired.

Systems and processes for operating a digital assistant are provided. Inaccordance with one or more examples, a method includes, at a userdevice with one or more processors and memory, receiving unstructurednatural language information from at least one user. The method alsoincludes, in response to receiving the unstructured natural languageinformation, determining whether event information is present in theunstructured natural language information. The method further includes,in accordance with a determination that event information is presentwithin the unstructured natural language information, determiningwhether an agreement on an event is present in the unstructured naturallanguage information. The method further includes, in accordance with adetermination that an agreement on an event is present, determining anevent type of the event and providing an event description based on theevent type.

Executable instructions for performing these functions are, optionally,included in a non-transitory computer-readable storage medium or othercomputer program product configured for execution by one or moreprocessors. Executable instructions for performing these functions are,optionally, included in a transitory computer-readable storage medium orother computer program product configured for execution by one or moreprocessors.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the various described embodiments,reference should be made to the Description of Embodiments below, inconjunction with the following drawings in which like reference numeralsrefer to corresponding parts throughout the figures.

FIG. 1 is a block diagram illustrating a system and environment forimplementing a digital assistant according to various examples.

FIG. 2A is a block diagram illustrating a portable multifunction deviceimplementing the client-side portion of a digital assistant inaccordance with some embodiments.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling according to various examples.

FIG. 3 illustrates a portable multifunction device implementing theclient-side portion of a digital assistant according to variousexamples.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface according to various examples.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on a portable multifunction device according to variousexamples.

FIG. 5B illustrates an exemplary user interface for a multifunctiondevice with a touch-sensitive surface that is separate from the displayaccording to various examples.

FIG. 6A illustrates a personal electronic device according to variousexamples.

FIG. 6B is a block diagram illustrating a personal electronic deviceaccording to various examples.

FIG. 7A is a block diagram illustrating a digital assistant system or aserver portion thereof according to various examples.

FIG. 7B illustrates the functions of the digital assistant shown in FIG.7A according to various examples.

FIG. 7C illustrates a portion of an ontology according to variousexamples.

FIG. 8 illustrates a block diagram of functions of a digital assistantaccording to various examples.

FIG. 9A illustrates an exemplary user interface of a user deviceaccording to various examples.

FIG. 9B illustrates an exemplary first network for event detection andclassification according to various examples.

FIG. 9C illustrates an exemplary first network for event detection andclassification according to various examples.

FIG. 10A illustrates an exemplary user interface of a user deviceaccording to various examples.

FIG. 10B illustrates an exemplary second network for event detection andclassification according to various examples.

FIG. 10C illustrates an exemplary second network for event detection andclassification according to various examples.

FIGS. 11A-11H illustrate an exemplary natural language event ontologyaccording to various examples.

FIG. 12A illustrates a block diagram of functions of an event typedetermination module according to various examples.

FIGS. 12B-12C illustrate an exemplary event type determination accordingto various examples.

FIG. 13 illustrates a block diagram of functions of a calendar entryprocess according to various examples.

FIGS. 14A-14J illustrate a flow diagram of an exemplary process fordetecting and classifying event using unstructured natural languageinformation according to various examples.

FIG. 15 illustrates a block diagram of an electronic device according tovarious examples.

DETAILED DESCRIPTION

In the following description of the disclosure and embodiments,reference is made to the accompanying drawings, in which it is shown byway of illustration, of specific embodiments that can be practiced. Itis to be understood that other embodiments and examples can bepracticed, and changes can be made without departing from the scope ofthe disclosure.

Techniques for detecting and classifying events from unstructurednatural language information are desirable. As described herein,techniques for detecting and classifying events from unstructurednatural language information are desired for various purposes, such asreducing the effort and cumbersomeness of manual calendar entry. Suchtechniques are advantageous because they allow event description to beaccurately extracted automatically from unstructured natural languageinformation despite its inherent ambiguity.

Although the following description uses terms “first,” “second,” etc. todescribe various elements, these elements should not be limited by theterms. These terms are only used to distinguish one element fromanother. For example, a first-context unit could be termed asecond-context unit and, similarly, a second-context unit could betermed a first-context unit, without departing from the scope of thevarious described examples. The first-context unit and thesecond-context unit can both be context units and, in some cases, can beseparate and different context units.

The terminology used in the description of the various describedexamples herein is for the purpose of describing particular examplesonly and is not intended to be limiting. As used in the description ofthe various described examples and the appended claims, the singularforms “a,” “an,” and “the” are intended to include the plural forms aswell, unless the context clearly indicates otherwise. It will also beunderstood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“includes,” “including,” “comprises,” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof

The term “if” may be construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” may be construed to mean “upon determining” or“in response to determining” or “upon detecting [the stated condition orevent]” or “in response to detecting [the stated condition or event],”depending on the context.

1. System and Environment

FIG. 1 illustrates a block diagram of system 100 according to variousexamples. In some examples, system 100 can implement a digitalassistant. The terms “digital assistant,” “virtual assistant,”“intelligent automated assistant,” or “automatic digital assistant” canrefer to any information processing system that interprets naturallanguage input in spoken and/or textual form to infer user intent, andperforms actions based on the inferred user intent. For example, to acton an inferred user intent, the system can perform one or more of thefollowing: identifying a task flow with steps and parameters designed toaccomplish the inferred user intent, inputting specific requirementsfrom the inferred user intent into the task flow; executing the taskflow by invoking programs, methods, services, APIs, or the like; andgenerating output responses to the user in an audible (e.g., speech)and/or visual form.

Specifically, a digital assistant can be capable of accepting a userrequest at least partially in the form of a natural language command,request, statement, narrative, and/or inquiry. Typically, the userrequest can seek either an informational answer or performance of a taskby the digital assistant. A satisfactory response to the user requestcan be a provision of the requested informational answer, a performanceof the requested task, or a combination of the two. For example, a usercan ask the digital assistant a question, such as “Where am I rightnow?” Based on the user's current location, the digital assistant cananswer, “You are in Central Park near the west gate.” The user can alsorequest the performance of a task, for example, “Please invite myfriends to my girlfriend's birthday party next week.” In response, thedigital assistant can acknowledge the request by saying “Yes, rightaway,” and then send a suitable calendar invite on behalf of the user toeach of the user's friends listed in the user's electronic address book.During performance of a requested task, the digital assistant cansometimes interact with the user in a continuous dialogue involvingmultiple exchanges of information over an extended period of time. Thereare numerous other ways of interacting with a digital assistant torequest information or performance of various tasks. In addition toproviding verbal responses and taking programmed actions, the digitalassistant can also provide responses in other visual or audio forms,e.g., as text, alerts, music, videos, animations, etc.

As shown in FIG. 1, in some examples, a digital assistant can beimplemented according to a client-server model. The digital assistantcan include client-side portion 102 (hereafter “DA client 102”) executedon user device 104 and server-side portion 106 (hereafter “DA server106”) executed on server system 108. DA client 102 can communicate withDA server 106 through one or more networks 110. DA client 102 canprovide client-side functionalities such as user-facing input and outputprocessing and communication with DA server 106. DA server 106 canprovide server-side functionalities for any number of DA clients 102each residing on a respective user device 104.

In some examples, DA server 106 can include client-facing I/O interface112, one or more processing modules 114, data and models 116, and I/Ointerface to external services 118. The client-facing I/O interface 112can facilitate the client-facing input and output processing for DAserver 106. One or more processing modules 114 can utilize data andmodels 116 to process speech input and determine the user's intent basedon natural language input. Further, one or more processing modules 114perform task execution based on inferred user intent. In some examples,DA server 106 can communicate with external services 120 throughnetwork(s) 110 for task completion or information acquisition. I/Ointerface to external services 118 can facilitate such communications.

User device 104 can be any suitable electronic device. For example, userdevices can be a portable multifunctional device (e.g., device 200,described below with reference to FIG. 2A), a multifunctional device(e.g., device 400, described below with reference to FIG. 4), or apersonal electronic device (e.g., device 600, described below withreference to FIG. 6A-B). A portable multifunctional device can be, forexample, a mobile telephone that also contains other functions, such asPDA and/or music player functions. Specific examples of portablemultifunction devices can include the iPhone®, iPod Touch®, and iPad®devices from Apple Inc. of Cupertino, Calif. Other examples of portablemultifunction devices can include, without limitation, laptop or tabletcomputers. Further, in some examples, user device 104 can be anon-portable multifunctional device. In particular, user device 104 canbe a desktop computer, a game console, a television, or a televisionset-top box. In some examples, user device 104 can include atouch-sensitive surface (e.g., touch screen displays and/or touchpads).Further, user device 104 can optionally include one or more otherphysical user-interface devices, such as a physical keyboard, a mouse,and/or a joystick. Various examples of electronic devices, such asmultifunctional devices, are described below in greater detail.

Examples of communication network(s) 110 can include local area networks(LAN) and wide area networks (WAN), e.g., the Internet. Communicationnetwork(s) 110 can be implemented using any known network protocol,including various wired or wireless protocols, such as, for example,Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), codedivision multiple access (CDMA), time division multiple access (TDMA),Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or anyother suitable communication protocol.

Server system 108 can be implemented on one or more standalone dataprocessing apparatus or a distributed network of computers. In someexamples, server system 108 can also employ various virtual devicesand/or services of third-party service providers (e.g., third-partycloud service providers) to provide the underlying computing resourcesand/or infrastructure resources of server system 108.

In some examples, user device 104 can communicate with DA server 106 viasecond user device 122. Second user device 122 can be similar oridentical to user device 104. For example, second user device 122 can besimilar to devices 200, 400, or 600 described below with reference toFIGS. 2A, 4, and 6A-B. User device 104 can be configured tocommunicatively couple to second user device 122 via a directcommunication connection, such as Bluetooth, NFC, BTLE, or the like, orvia a wired or wireless network, such as a local Wi-Fi network. In someexamples, second user device 122 can be configured to act as a proxybetween user device 104 and DA server 106. For example, DA client 102 ofuser device 104 can be configured to transmit information (e.g., a userrequest received at user device 104) to DA server 106 via second userdevice 122. DA server 106 can process the information and returnrelevant data (e.g., data content responsive to the user request) touser device 104 via second user device 122.

In some examples, user device 104 can be configured to communicateabbreviated requests for data to second user device 122 to reduce theamount of information transmitted from user device 104. Second userdevice 122 can be configured to determine supplemental information toadd to the abbreviated request to generate a complete request totransmit to DA server 106. This system architecture can advantageouslyallow user device 104 having limited communication capabilities and/orlimited battery power (e.g., a watch or a similar compact electronicdevice) to access services provided by DA server 106 by using seconduser device 122, having greater communication capabilities and/orbattery power (e.g., a mobile phone, laptop computer, tablet computer,or the like), as a proxy to DA server 106. While only two user devices104 and 122 are shown in FIG. 1, it should be appreciated that system100 can include any number and type of user devices configured in thisproxy configuration to communicate with DA server system 106.

Although the digital assistant shown in FIG. 1 can include both aclient-side portion (e.g., DA client 102) and a server-side portion(e.g., DA server 106), in some examples, the functions of a digitalassistant can be implemented as a standalone application installed on auser device. In addition, the divisions of functionalities between theclient and server portions of the digital assistant can vary indifferent implementations. For instance, in some examples, the DA clientcan be a thin-client that provides only user-facing input and outputprocessing functions, and delegates all other functionalities of thedigital assistant to a backend server.

2. Electronic Devices

Attention is now directed toward embodiments of electronic devices forimplementing the client-side portion of a digital assistant. FIG. 2A isa block diagram illustrating portable multifunction device 200 withtouch-sensitive display system 212 in accordance with some embodiments.Touch-sensitive display 212 is sometimes called a “touch screen” forconvenience and is sometimes known as or called a “touch-sensitivedisplay system.” Device 200 includes memory 202 (which optionallyincludes one or more computer-readable storage mediums), memorycontroller 222, one or more processing units (CPUs) 220, peripheralsinterface 218, RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, input/output (I/O) subsystem 206, other input controldevices 216, and external port 224. Device 200 optionally includes oneor more optical sensors 264. Device 200 optionally includes one or morecontact intensity sensors 265 for detecting intensity of contacts ondevice 200 (e.g., a touch-sensitive surface such as touch-sensitivedisplay system 212 of device 200). Device 200 optionally includes one ormore tactile output generators 267 for generating tactile outputs ondevice 200 (e.g., generating tactile outputs on a touch-sensitivesurface such as touch-sensitive display system 212 of device 200 ortouchpad 455 of device 400). These components optionally communicateover one or more communication buses or signal lines 203.

As used in the specification and claims, the term “intensity” of acontact on a touch-sensitive surface refers to the force or pressure(force per unit area) of a contact (e.g., a finger contact) on thetouch-sensitive surface or to a substitute (proxy) for the force orpressure of a contact on the touch-sensitive surface. The intensity of acontact has a range of values that includes at least four distinctvalues and more typically includes hundreds of distinct values (e.g., atleast 256). Intensity of a contact is, optionally, determined (ormeasured) using various approaches and various sensors or combinationsof sensors. For example, one or more force sensors underneath oradjacent to the touch-sensitive surface are, optionally, used to measureforce at various points on the touch-sensitive surface. In someimplementations, force measurements from multiple force sensors arecombined (e.g., a weighted average) to determine an estimated force of acontact. Similarly, a pressure-sensitive tip of a stylus is, optionally,used to determine a pressure of the stylus on the touch-sensitivesurface. Alternatively, the size of the contact area detected on thetouch-sensitive surface and/or changes thereto, the capacitance of thetouch-sensitive surface proximate to the contact and/or changes thereto,and/or the resistance of the touch-sensitive surface proximate to thecontact and/or changes thereto are, optionally, used as a substitute forthe force or pressure of the contact on the touch-sensitive surface. Insome implementations, the substitute measurements for contact force orpressure are used directly to determine whether an intensity thresholdhas been exceeded (e.g., the intensity threshold is described in unitscorresponding to the substitute measurements). In some implementations,the substitute measurements for contact force or pressure are convertedto an estimated force or pressure, and the estimated force or pressureis used to determine whether an intensity threshold has been exceeded(e.g., the intensity threshold is a pressure threshold measured in unitsof pressure). Using the intensity of a contact as an attribute of a userinput allows for user access to additional device functionality that mayotherwise not be accessible by the user on a reduced-size device withlimited real estate for displaying affordances (e.g., on atouch-sensitive display) and/or receiving user input (e.g., via atouch-sensitive display, a touch-sensitive surface, or aphysical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output”refers to physical displacement of a device relative to a previousposition of the device, physical displacement of a component (e.g., atouch-sensitive surface) of a device relative to another component(e.g., housing) of the device, or displacement of the component relativeto a center of mass of the device that will be detected by a user withthe user's sense of touch. For example, in situations where the deviceor the component of the device is in contact with a surface of a userthat is sensitive to touch (e.g., a finger, palm, or other part of auser's hand), the tactile output generated by the physical displacementwill be interpreted by the user as a tactile sensation corresponding toa perceived change in physical characteristics of the device or thecomponent of the device. For example, movement of a touch-sensitivesurface (e.g., a touch-sensitive display or trackpad) is, optionally,interpreted by the user as a “down click” or “up click” of a physicalactuator button. In some cases, a user will feel a tactile sensationsuch as an “down click” or “up click” even when there is no movement ofa physical actuator button associated with the touch-sensitive surfacethat is physically pressed (e.g., displaced) by the user's movements. Asanother example, movement of the touch-sensitive surface is, optionally,interpreted or sensed by the user as “roughness” of the touch-sensitivesurface, even when there is no change in smoothness of thetouch-sensitive surface. While such interpretations of touch by a userwill be subject to the individualized sensory perceptions of the user,there are many sensory perceptions of touch that are common to a largemajority of users. Thus, when a tactile output is described ascorresponding to a particular sensory perception of a user (e.g., an “upclick,” a “down click,” “roughness”), unless otherwise stated, thegenerated tactile output corresponds to physical displacement of thedevice or a component thereof that will generate the described sensoryperception for a typical (or average) user.

It should be appreciated that device 200 is only one example of aportable multifunction device, and that device 200 optionally has moreor fewer components than shown, optionally combines two or morecomponents, or optionally has a different configuration or arrangementof the components. The various components shown in FIG. 2A areimplemented in hardware, software, or a combination of both hardware andsoftware, including one or more signal processing and/orapplication-specific integrated circuits.

Memory 202 may include one or more computer-readable storage mediums.The computer-readable storage mediums may be tangible andnon-transitory. Memory 202 may include high-speed random access memoryand may also include non-volatile memory, such as one or more magneticdisk storage devices, flash memory devices, or other non-volatilesolid-state memory devices. Memory controller 222 may control access tomemory 202 by other components of device 200.

In some examples, a non-transitory computer-readable storage medium ofmemory 202 can be used to store instructions (e.g., for performingaspects of process 1200, described below) for use by or in connectionwith an instruction execution system, apparatus, or device, such as acomputer-based system, processor-containing system, or other system thatcan fetch the instructions from the instruction execution system,apparatus, or device and execute the instructions. In other examples,the instructions (e.g., for performing aspects of process 1200,described below) can be stored on a non-transitory computer-readablestorage medium (not shown) of the server system 108 or can be dividedbetween the non-transitory computer-readable storage medium of memory202 and the non-transitory computer-readable storage medium of serversystem 108. In the context of this document, a “non-transitorycomputer-readable storage medium” can be any medium that can contain orstore the program for use by or in connection with the instructionexecution system, apparatus, or device.

Peripherals interface 218 can be used to couple input and outputperipherals of the device to CPU 220 and memory 202. The one or moreprocessors 220 run or execute various software programs and/or sets ofinstructions stored in memory 202 to perform various functions fordevice 200 and to process data. In some embodiments, peripheralsinterface 218, CPU 220, and memory controller 222 may be implemented ona single chip, such as chip 204. In some other embodiments, they may beimplemented on separate chips.

RF (radio frequency) circuitry 208 receives and sends RF signals, alsocalled electromagnetic signals. RF circuitry 208 converts electricalsignals to/from electromagnetic signals and communicates withcommunications networks and other communications devices via theelectromagnetic signals. RF circuitry 208 optionally includes well-knowncircuitry for performing these functions, including but not limited toan antenna system, an RF transceiver, one or more amplifiers, a tuner,one or more oscillators, a digital signal processor, a CODEC chipset, asubscriber identity module (SIM) card, memory, and so forth. RFcircuitry 208 optionally communicates with networks, such as theInternet, also referred to as the World Wide Web (WWW), an intranetand/or a wireless network, such as a cellular telephone network, awireless local area network (LAN) and/or a metropolitan area network(MAN), and other devices by wireless communication. The RF circuitry 208optionally includes well-known circuitry for detecting near fieldcommunication (NFC) fields, such as by a short-range communicationradio. The wireless communication optionally uses any of a plurality ofcommunications standards, protocols, and technologies, including but notlimited to Global System for Mobile Communications (GSM), Enhanced DataGSM Environment (EDGE), high-speed downlink packet access (HSDPA),high-speed uplink packet access (HSDPA), Evolution, Data-Only (EV-DO),HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), nearfield communication (NFC), wideband code division multiple access(W-CDMA), code division multiple access (CDMA), time division multipleaccess (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity(Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n,and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, aprotocol for e mail (e.g., Internet message access protocol (IMAP)and/or post office protocol (POP)), instant messaging (e.g., extensiblemessaging and presence protocol (XMPP), Session Initiation Protocol forInstant Messaging and Presence Leveraging Extensions (SIMPLE), InstantMessaging and Presence Service (IMPS)), and/or Short Message Service(SMS), or any other suitable communication protocol, includingcommunication protocols not yet developed as of the filing date of thisdocument.

Audio circuitry 210, speaker 211, and microphone 213 provide an audiointerface between a user and device 200. Audio circuitry 210 receivesaudio data from peripherals interface 218, converts the audio data to anelectrical signal, and transmits the electrical signal to speaker 211.Speaker 211 converts the electrical signal to human-audible sound waves.Audio circuitry 210 also receives electrical signals converted bymicrophone 213 from sound waves. Audio circuitry 210 converts theelectrical signal to audio data and transmits the audio data toperipherals interface 218 for processing. Audio data may be retrievedfrom and/or transmitted to memory 202 and/or RF circuitry 208 byperipherals interface 218. In some embodiments, audio circuitry 210 alsoincludes a headset jack (e.g., 312, FIG. 3). The headset jack providesan interface between audio circuitry 210 and removable audioinput/output peripherals, such as output-only headphones or a headsetwith both output (e.g., a headphone for one or both ears) and input(e.g., a microphone).

I/O subsystem 206 couples input/output peripherals on device 200, suchas touch screen 212 and other input control devices 216, to peripheralsinterface 218. I/O subsystem 206 optionally includes display controller256, optical sensor controller 258, intensity sensor controller 259,haptic feedback controller 261, and one or more input controllers 260for other input or control devices. The one or more input controllers260 receive/send electrical signals from/to other input control devices216. The other input control devices 216 optionally include physicalbuttons (e.g., push buttons, rocker buttons, etc.), dials, sliderswitches, joysticks, click wheels, and so forth. In some alternateembodiments, input controller(s) 260 are, optionally, coupled to any (ornone) of the following: a keyboard, an infrared port, a USB port, and apointer device such as a mouse. The one or more buttons (e.g., 308, FIG.3) optionally include an up/down button for volume control of speaker211 and/or microphone 213. The one or more buttons optionally include apush button (e.g., 306, FIG. 3).

A quick press of the push button may disengage a lock of touch screen212 or begin a process that uses gestures on the touch screen to unlockthe device, as described in U.S. patent application Ser. No. 11/322,549,“Unlocking a Device by Performing Gestures on an Unlock Image,” filedDec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated byreference in its entirety. A longer press of the push button (e.g., 306)may turn power to device 200 on or off. The user may be able tocustomize a functionality of one or more of the buttons. Touch screen212 is used to implement virtual or soft buttons and one or more softkeyboards.

Touch-sensitive display 212 provides an input interface and an outputinterface between the device and a user. Display controller 256 receivesand/or sends electrical signals from/to touch screen 212. Touch screen212 displays visual output to the user. The visual output may includegraphics, text, icons, video, and any combination thereof (collectivelytermed “graphics”). In some embodiments, some or all of the visualoutput may correspond to user-interface objects.

Touch screen 212 has a touch-sensitive surface, sensor, or set ofsensors that accepts input from the user based on haptic and/or tactilecontact. Touch screen 212 and display controller 256 (along with anyassociated modules and/or sets of instructions in memory 202) detectcontact (and any movement or breaking of the contact) on touch screen212 and convert the detected contact into interaction withuser-interface objects (e.g., one or more soft keys, icons, web pages,or images) that are displayed on touch screen 212. In an exemplaryembodiment, a point of contact between touch screen 212 and the usercorresponds to a finger of the user.

Touch screen 212 may use LCD (liquid crystal display) technology, LPD(light emitting polymer display) technology, or LED (light emittingdiode) technology, although other display technologies may be used inother embodiments. Touch screen 212 and display controller 256 maydetect contact and any movement or breaking thereof using any of aplurality of touch sensing technologies now known or later developed,including but not limited to capacitive, resistive, infrared, andsurface acoustic wave technologies, as well as other proximity sensorarrays or other elements for determining one or more points of contactwith touch screen 212. In an exemplary embodiment, projected mutualcapacitance sensing technology is used, such as that found in theiPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 212 may beanalogous to the multi-touch sensitive touchpads described in thefollowing U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No.6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932(Westerman), and/or U.S. Patent Publication 2002/0015024A1, each ofwhich is hereby incorporated by reference in its entirety. However,touch screen 212 displays visual output from device 200, whereastouch-sensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 212 may beas described in the following applications: (1) U.S. patent applicationSer. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2,2006; (2) U.S. patent application Ser. No. 10/840,862, “MultipointTouchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No.10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30,2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures ForTouch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patentapplication Ser. No. 11/038,590, “Mode-Based Graphical User InterfacesFor Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patentapplication Ser. No. 11/228,758, “Virtual Input Device Placement On ATouch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patentapplication Ser. No. 11/228,700, “Operation Of A Computer With A TouchScreen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser.No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen VirtualKeyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No.11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. Allof these applications are incorporated by reference herein in theirentirety.

Touch screen 212 may have a video resolution in excess of 100 dpi. Insome embodiments, the touch screen has a video resolution ofapproximately 160 dpi. The user may make contact with touch screen 212using any suitable object or appendage, such as a stylus, a finger, andso forth. In some embodiments, the user interface is designed to workprimarily with finger-based contacts and gestures, which can be lessprecise than stylus-based input due to the larger area of contact of afinger on the touch screen. In some embodiments, the device translatesthe rough finger-based input into a precise pointer/cursor position orcommand for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 200 mayinclude a touchpad (not shown) for activating or deactivating particularfunctions. In some embodiments, the touchpad is a touch-sensitive areaof the device that, unlike the touch screen, does not display visualoutput. The touchpad may be a touch-sensitive surface that is separatefrom touch screen 212 or an extension of the touch-sensitive surfaceformed by the touch screen.

Device 200 also includes power system 262 for powering the variouscomponents. Power system 262 may include a power management system, oneor more power sources (e.g., battery, alternating current (AC)), arecharging system, a power failure detection circuit, a power converteror inverter, a power status indicator (e.g., a light-emitting diode(LED)) and any other components associated with the generation,management and distribution of power in portable devices.

Device 200 may also include one or more optical sensors 264. FIG. 2Ashows an optical sensor coupled to optical sensor controller 258 in I/Osubsystem 206. Optical sensor 264 may include charge-coupled device(CCD) or complementary metal-oxide semiconductor (CMOS)phototransistors. Optical sensor 264 receives light from theenvironment, projected through one or more lenses, and converts thelight to data representing an image. In conjunction with imaging module243 (also called a camera module), optical sensor 264 may capture stillimages or video. In some embodiments, an optical sensor is located onthe back of device 200, opposite touch screen display 212 on the frontof the device so that the touch screen display may be used as aviewfinder for still and/or video image acquisition. In someembodiments, an optical sensor is located on the front of the device sothat the user's image may be obtained for video conferencing while theuser views the other video conference participants on the touch screendisplay. In some embodiments, the position of optical sensor 264 can bechanged by the user (e.g., by rotating the lens and the sensor in thedevice housing) so that a single optical sensor 264 may be used alongwith the touch screen display for both video conferencing and stilland/or video image acquisition.

Device 200 optionally also includes one or more contact intensitysensors 265. FIG. 2A shows a contact intensity sensor coupled tointensity sensor controller 259 in I/O subsystem 206. Contact intensitysensor 265 optionally includes one or more piezoresistive strain gauges,capacitive force sensors, electric force sensors, piezoelectric forcesensors, optical force sensors, capacitive touch-sensitive surfaces, orother intensity sensors (e.g., sensors used to measure the force (orpressure) of a contact on a touch-sensitive surface). Contact intensitysensor 265 receives contact intensity information (e.g., pressureinformation or a proxy for pressure information) from the environment.In some embodiments, at least one contact intensity sensor is collocatedwith, or proximate to, a touch-sensitive surface (e.g., touch-sensitivedisplay system 212). In some embodiments, at least one contact intensitysensor is located on the back of device 200, opposite touch screendisplay 212, which is located on the front of device 200.

Device 200 may also include one or more proximity sensors 266. FIG. 2Ashows proximity sensor 266 coupled to peripherals interface 218.Alternately, proximity sensor 266 may be coupled to input controller 260in I/O subsystem 206. Proximity sensor 266 may perform as described inU.S. patent application Ser. No. 11/241,839, “Proximity Detector InHandheld Device”; Ser. No. 11/240,788, “Proximity Detector In HandheldDevice”; Ser. No. 11/620,702, “Using Ambient Light Sensor To AugmentProximity Sensor Output”; Ser. No. 11/586,862, “Automated Response ToAnd Sensing Of User Activity In Portable Devices”; and Ser. No.11/638,251, “Methods And Systems For Automatic Configuration OfPeripherals,” which are hereby incorporated by reference in theirentirety. In some embodiments, the proximity sensor turns off anddisables touch screen 212 when the multifunction device is placed nearthe user's ear (e.g., when the user is making a phone call).

Device 200 optionally also includes one or more tactile outputgenerators 267. FIG. 2A shows a tactile output generator coupled tohaptic feedback controller 261 in I/O subsystem 206. Tactile outputgenerator 267 optionally includes one or more electroacoustic devicessuch as speakers or other audio components and/or electromechanicaldevices that convert energy into linear motion such as a motor,solenoid, electroactive polymer, piezoelectric actuator, electrostaticactuator, or other tactile output generating component (e.g., acomponent that converts electrical signals into tactile outputs on thedevice). Contact intensity sensor 265 receives tactile feedbackgeneration instructions from haptic feedback module 233 and generatestactile outputs on device 200 that are capable of being sensed by a userof device 200. In some embodiments, at least one tactile outputgenerator is collocated with, or proximate to, a touch-sensitive surface(e.g., touch-sensitive display system 212) and, optionally, generates atactile output by moving the touch-sensitive surface vertically (e.g.,in/out of a surface of device 200) or laterally (e.g., back and forth inthe same plane as a surface of device 200). In some embodiments, atleast one tactile output generator sensor is located on the back ofdevice 200, opposite touch screen display 212, which is located on thefront of device 200.

Device 200 may also include one or more accelerometers 268. FIG. 2Ashows accelerometer 268 coupled to peripherals interface 218.Alternately, accelerometer 268 may be coupled to an input controller 260in I/O subsystem 206. Accelerometer 268 may perform as described in U.S.Patent Publication No. 20050190059, “Acceleration-based Theft DetectionSystem for Portable Electronic Devices,” and U.S. Patent Publication No.20060017692, “Methods And Apparatuses For Operating A Portable DeviceBased On An Accelerometer,” both of which are incorporated by referenceherein in their entirety. In some embodiments, information is displayedon the touch screen display in a portrait view or a landscape view basedon an analysis of data received from the one or more accelerometers.Device 200 optionally includes, in addition to accelerometer(s) 268, amagnetometer (not shown) and a GPS (or GLONASS or other globalnavigation system) receiver (not shown) for obtaining informationconcerning the location and orientation (e.g., portrait or landscape) ofdevice 200.

In some embodiments, the software components stored in memory 202include operating system 226, communication module (or set ofinstructions) 228, contact/motion module (or set of instructions) 230,graphics module (or set of instructions) 232, text input module (or setof instructions) 234, Global Positioning System (GPS) module (or set ofinstructions) 235, Digital Assistant Client Module 229, and applications(or sets of instructions) 236. Further, memory 202 can store data andmodels, such as user data and models 231. Furthermore, in someembodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/globalinternal state 257, as shown in FIGS. 2A and 4. Device/global internalstate 257 includes one or more of: active application state, indicatingwhich applications, if any, are currently active; display state,indicating what applications, views, or other information occupy variousregions of touch screen display 212; sensor state, including informationobtained from the device's various sensors and input control devices216; and location information concerning the device's location and/orattitude.

Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communication between varioushardware and software components.

Communication module 228 facilitates communication with other devicesover one or more external ports 224 and also includes various softwarecomponents for handling data received by RF circuitry 208 and/orexternal port 224. External port 224 (e.g., Universal Serial Bus (USB),FIREWIRE, etc.) is adapted for coupling directly to other devices orindirectly over a network (e.g., the Internet, wireless LAN, etc.). Insome embodiments, the external port is a multi-pin (e.g., 30-pin)connector that is the same as, or similar to and/or compatible with, the30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 230 optionally detects contact with touch screen212 (in conjunction with display controller 256) and othertouch-sensitive devices (e.g., a touchpad or physical click wheel).Contact/motion module 230 includes various software components forperforming various operations related to detection of contact, such asdetermining if contact has occurred (e.g., detecting a finger-downevent), determining an intensity of the contact (e.g., the force orpressure of the contact or a substitute for the force or pressure of thecontact), determining if there is movement of the contact and trackingthe movement across the touch-sensitive surface (e.g., detecting one ormore finger-dragging events), and determining if the contact has ceased(e.g., detecting a finger-up event or a break in contact).Contact/motion module 230 receives contact data from the touch-sensitivesurface. Determining movement of the point of contact, which isrepresented by a series of contact data, optionally includes determiningspeed (magnitude), velocity (magnitude and direction), and/or anacceleration (a change in magnitude and/or direction) of the point ofcontact. These operations are, optionally, applied to single contacts(e.g., one finger contacts) or to multiple simultaneous contacts (e.g.,“multitouch”/multiple finger contacts). In some embodiments,contact/motion module 230 and display controller 256 detect contact on atouchpad.

In some embodiments, contact/motion module 230 uses a set of one or moreintensity thresholds to determine whether an operation has beenperformed by a user (e.g., to determine whether a user has “clicked” onan icon). In some embodiments, at least a subset of the intensitythresholds are determined in accordance with software parameters (e.g.,the intensity thresholds are not determined by the activation thresholdsof particular physical actuators and can be adjusted without changingthe physical hardware of device 200). For example, a mouse “click”threshold of a trackpad or touch screen display can be set to any of alarge range of predefined threshold values without changing the trackpador touch screen display hardware. Additionally, in some implementations,a user of the device is provided with software settings for adjustingone or more of the set of intensity thresholds (e.g., by adjustingindividual intensity thresholds and/or by adjusting a plurality ofintensity thresholds at once with a system-level click “intensity”parameter).

Contact/motion module 230 optionally detects a gesture input by a user.Different gestures on the touch-sensitive surface have different contactpatterns (e.g., different motions, timings, and/or intensities ofdetected contacts). Thus, a gesture is, optionally, detected bydetecting a particular contact pattern. For example, detecting a fingertap gesture includes detecting a finger-down event followed by detectinga finger-up (liftoff) event at the same position (or substantially thesame position) as the finger-down event (e.g., at the position of anicon). As another example, detecting a finger swipe gesture on thetouch-sensitive surface includes detecting a finger-down event followedby detecting one or more finger-dragging events, and subsequentlyfollowed by detecting a finger-up (liftoff) event.

Graphics module 232 includes various known software components forrendering and displaying graphics on touch screen 212 or other display,including components for changing the visual impact (e.g., brightness,transparency, saturation, contrast, or other visual property) ofgraphics that are displayed. As used herein, the term “graphics”includes any object that can be displayed to a user, including, withoutlimitation, text, web pages, icons (such as user-interface objectsincluding soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 232 stores data representinggraphics to be used. Each graphic is, optionally, assigned acorresponding code. Graphics module 232 receives, from applicationsetc., one or more codes specifying graphics to be displayed along with,if necessary, coordinate data and other graphic property data and thengenerates screen image data to output to display controller 256.

Haptic feedback module 233 includes various software components forgenerating instructions used by tactile output generator(s) 267 toproduce tactile outputs at one or more locations on device 200 inresponse to user interactions with device 200.

Text input module 234, which may be a component of graphics module 232,provides soft keyboards for entering text in various applications (e.g.,contacts 237, email 240, IM 241, browser 247, and any other applicationthat needs text input).

GPS module 235 determines the location of the device and provides thisinformation for use in various applications (e.g., to telephone 238 foruse in location-based dialing; to camera 243 as picture/video metadata;and to applications that provide location-based services such as weatherwidgets, local yellow page widgets, and map/navigation widgets).

Digital assistant client module 229 can include various client-sidedigital assistant instructions to provide the client-sidefunctionalities of the digital assistant. For example, digital assistantclient module 229 can be capable of accepting voice input (e.g., speechinput), text input, touch input, and/or gestural input through varioususer interfaces (e.g., microphone 213, accelerometer(s) 268,touch-sensitive display system 212, optical sensor(s) 264, other inputcontrol devices 216, etc.) of portable multifunction device 200. Digitalassistant client module 229 can also be capable of providing output inaudio (e.g., speech output), visual, and/or tactile forms throughvarious output interfaces (e.g., speaker 211, touch-sensitive displaysystem 212, tactile output generator(s) 267, etc.) of portablemultifunction device 200. For example, output can be provided as voice,sound, alerts, text messages, menus, graphics, videos, animations,vibrations, and/or combinations of two or more of the above. Duringoperation, digital assistant client module 229 can communicate with DAserver 106 using RF circuitry 208.

User data and models 231 can include various data associated with theuser (e.g., user-specific vocabulary data, user preference data,user-specified name pronunciations, data from the user's electronicaddress book, to-do lists, shopping lists, etc.) to provide theclient-side functionalities of the digital assistant. Further, user dataand models 231 can includes various models (e.g., speech recognitionmodels, statistical language models, natural language processing models,ontology, task flow models, service models, etc.) for processing userinput and determining user intent.

In some examples, digital assistant client module 229 can utilize thevarious sensors, subsystems, and peripheral devices of portablemultifunction device 200 to gather additional information from thesurrounding environment of the portable multifunction device 200 toestablish a context associated with a user, the current userinteraction, and/or the current user input. In some examples, digitalassistant client module 229 can provide the contextual information or asubset thereof with the user input to DA server 106 to help infer theuser's intent. In some examples, the digital assistant can also use thecontextual information to determine how to prepare and deliver outputsto the user. Contextual information can be referred to as context data.

In some examples, the contextual information that accompanies the userinput can include sensor information, e.g., lighting, ambient noise,ambient temperature, images or videos of the surrounding environment,etc. In some examples, the contextual information can also include thephysical state of the device, e.g., device orientation, device location,device temperature, power level, speed, acceleration, motion patterns,cellular signals strength, etc. In some examples, information related tothe software state of DA server 106, e.g., running processes, installedprograms, past and present network activities, background services,error logs, resources usage, etc., and of portable multifunction device200 can be provided to DA server 106 as contextual informationassociated with a user input.

In some examples, the digital assistant client module 229 canselectively provide information (e.g., user data 231) stored on theportable multifunction device 200 in response to requests from DA server106. In some examples, digital assistant client module 229 can alsoelicit additional input from the user via a natural language dialogue orother user interfaces upon request by DA server 106. Digital assistantclient module 229 can pass the additional input to DA server 106 to helpDA server 106 in intent deduction and/or fulfillment of the user'sintent expressed in the user request.

A more detailed description of a digital assistant is described belowwith reference to FIGS. 7A-C. It should be recognized that digitalassistant client module 229 can include any number of the sub-modules ofdigital assistant module 726 described below.

Applications 236 may include the following modules (or sets ofinstructions), or a subset or superset thereof:

-   -   Contacts module 237 (sometimes called an address book or contact        list);    -   Telephone module 238;    -   Video conference module 239;    -   Email client module 240;    -   Instant messaging (IM) module 241;    -   Workout support module 242;    -   Camera module 243 for still and/or video images;    -   Image management module 244;    -   Video player module;    -   Music player module;    -   Browser module 247;    -   Calendar module 248;    -   Widget modules 249, which may include one or more of: weather        widget 249-1, stocks widget 249-2, calculator widget 249-3,        alarm clock widget 249-4, dictionary widget 249-5, and other        widgets obtained by the user, as well as user-created widgets        249-6;    -   Widget creator module 250 for making user-created widgets 249-6;    -   Search module 251;    -   Video and music player module 252, which merges video player        module and music player module;    -   Notes module 253;    -   Map module 254; and/or    -   Online video module 255.

Examples of other applications 236 that may be stored in memory 202include other word processing applications, other image editingapplications, drawing applications, presentation applications,JAVA-enabled applications, encryption, digital rights management, voicerecognition, and voice replication.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, contacts module 237 may be used to manage an address book orcontact list (e.g., stored in application internal state 292 of contactsmodule 237 in memory 202 or memory 470), including: adding name(s) tothe address book; deleting name(s) from the address book; associatingtelephone number(s), email address(es), physical address(es) or otherinformation with a name; associating an image with a name; categorizingand sorting names; providing telephone numbers or email addresses toinitiate and/or facilitate communications by telephone 238, videoconference module 239, email 240, or IM 241; and so forth.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, contact/motionmodule 230, graphics module 232, and text input module 234, telephonemodule 238 may be used to enter a sequence of characters correspondingto a telephone number, access one or more telephone numbers in contactsmodule 237, modify a telephone number that has been entered, dial arespective telephone number, conduct a conversation, and disconnect orhang up when the conversation is completed. As noted above, the wirelesscommunication may use any of a plurality of communications standards,protocols, and technologies.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, optical sensor264, optical sensor controller 258, contact/motion module 230, graphicsmodule 232, text input module 234, contacts module 237, and telephonemodule 238, video conference module 239 includes executable instructionsto initiate, conduct, and terminate a video conference between a userand one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, email client module 240 includes executableinstructions to create, send, receive, and manage email in response touser instructions. In conjunction with image management module 244,email client module 240 makes it very easy to create and send emailswith still or video images taken with camera module 243.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, instant messaging module 241 includes executableinstructions to enter a sequence of characters corresponding to aninstant message, to modify previously entered characters, to transmit arespective instant message (for example, using a Short Message Service(SMS) or Multimedia Message Service (MMS) protocol for telephony-basedinstant messages or using XMPP, SIMPLE, or IMPS for Internet-basedinstant messages), to receive instant messages, and to view receivedinstant messages. In some embodiments, transmitted and/or receivedinstant messages may include graphics, photos, audio files, video files,and/or other attachments as are supported in an MMS and/or an EnhancedMessaging Service (EMS). As used herein, “instant messaging” refers toboth telephony-based messages (e.g., messages sent using SMS or MMS) andInternet-based messages (e.g., messages sent using XMPP, SIMPLE, orIMPS).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, map module 254, and music playermodule, workout support module 242 includes executable instructions tocreate workouts (e.g., with time, distance, and/or calorie burninggoals); communicate with workout sensors (sports devices); receiveworkout sensor data; calibrate sensors used to monitor a workout; selectand play music for a workout; and display, store, and transmit workoutdata.

In conjunction with touch screen 212, display controller 256, opticalsensor(s) 264, optical sensor controller 258, contact/motion module 230,graphics module 232, and image management module 244, camera module 243includes executable instructions to capture still images or video(including a video stream) and store them into memory 202, modifycharacteristics of a still image or video, or delete a still image orvideo from memory 202.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, text input module 234,and camera module 243, image management module 244 includes executableinstructions to arrange, modify (e.g., edit), or otherwise manipulate,label, delete, present (e.g., in a digital slide show or album), andstore still and/or video images.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, browser module 247 includes executable instructions tobrowse the Internet in accordance with user instructions, includingsearching, linking to, receiving, and displaying web pages or portionsthereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, email client module 240, and browser module 247,calendar module 248 includes executable instructions to create, display,modify, and store calendars and data associated with calendars (e.g.,calendar entries, to-do lists, etc.) in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, widget modules 249 aremini-applications that may be downloaded and used by a user (e.g.,weather widget 249-1, stocks widget 249-2, calculator widget 249-3,alarm clock widget 249-4, and dictionary widget 249-5) or created by theuser (e.g., user-created widget 249-6). In some embodiments, a widgetincludes an HTML (Hypertext Markup Language) file, a CSS (CascadingStyle Sheets) file, and a JavaScript file. In some embodiments, a widgetincludes an XML (Extensible Markup Language) file and a JavaScript file(e.g., Yahoo! Widgets).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, the widget creator module 250may be used by a user to create widgets (e.g., turning a user-specifiedportion of a web page into a widget).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, search module 251 includes executable instructions to search fortext, music, sound, image, video, and/or other files in memory 202 thatmatch one or more search criteria (e.g., one or more user-specifiedsearch terms) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, and browser module 247, video and musicplayer module 252 includes executable instructions that allow the userto download and play back recorded music and other sound files stored inone or more file formats, such as MP3 or AAC files, and executableinstructions to display, present, or otherwise play back videos (e.g.,on touch screen 212 or on an external, connected display via externalport 224). In some embodiments, device 200 optionally includes thefunctionality of an MP3 player, such as an iPod (trademark of AppleInc.).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, notes module 253 includes executable instructions to create andmanage notes, to-do lists, and the like in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, and browser module 247, map module 254may be used to receive, display, modify, and store maps and dataassociated with maps (e.g., driving directions, data on stores and otherpoints of interest at or near a particular location, and otherlocation-based data) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, text input module 234, email clientmodule 240, and browser module 247, online video module 255 includesinstructions that allow the user to access, browse, receive (e.g., bystreaming and/or download), play back (e.g., on the touch screen or onan external, connected display via external port 224), send an emailwith a link to a particular online video, and otherwise manage onlinevideos in one or more file formats, such as H.264. In some embodiments,instant messaging module 241, rather than email client module 240, isused to send a link to a particular online video. Additional descriptionof the online video application can be found in U.S. Provisional PatentApplication No. 60/936,562, “Portable Multifunction Device, Method, andGraphical User Interface for Playing Online Videos,” filed Jun. 20,2007, and U.S. patent application Ser. No. 11/968,067, “PortableMultifunction Device, Method, and Graphical User Interface for PlayingOnline Videos,” filed Dec. 31, 2007, the contents of which are herebyincorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to aset of executable instructions for performing one or more functionsdescribed above and the methods described in this application (e.g., thecomputer-implemented methods and other information processing methodsdescribed herein). These modules (e.g., sets of instructions) need notbe implemented as separate software programs, procedures, or modules,and thus various subsets of these modules may be combined or otherwiserearranged in various embodiments. For example, video player module maybe combined with music player module into a single module (e.g., videoand music player module 252, FIG. 2A). In some embodiments, memory 202may store a subset of the modules and data structures identified above.Furthermore, memory 202 may store additional modules and data structuresnot described above.

In some embodiments, device 200 is a device where operation of apredefined set of functions on the device is performed exclusivelythrough a touch screen and/or a touchpad. By using a touch screen and/ora touchpad as the primary input control device for operation of device200, the number of physical input control devices (such as push buttons,dials, and the like) on device 200 may be reduced.

The predefined set of functions that are performed exclusively through atouch screen and/or a touchpad optionally include navigation betweenuser interfaces. In some embodiments, the touchpad, when touched by theuser, navigates device 200 to a main, home, or root menu from any userinterface that is displayed on device 200. In such embodiments, a “menubutton” is implemented using a touchpad. In some other embodiments, themenu button is a physical push button or other physical input controldevice instead of a touchpad.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments. In some embodiments,memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., inoperating system 226) and a respective application 236-1 (e.g., any ofthe aforementioned applications 237-251, 255, 480-490).

Event sorter 270 receives event information and determines theapplication 236-1 and application view 291 of application 236-1 to whichto deliver the event information. Event sorter 270 includes eventmonitor 271 and event dispatcher module 274. In some embodiments,application 236-1 includes application internal state 292, whichindicates the current application view(s) displayed on touch-sensitivedisplay 212 when the application is active or executing. In someembodiments, device/global internal state 257 is used by event sorter270 to determine which application(s) is (are) currently active, andapplication internal state 292 is used by event sorter 270 to determineapplication views 291 to which to deliver event information.

In some embodiments, application internal state 292 includes additionalinformation, such as one or more of: resume information to be used whenapplication 236-1 resumes execution, user interface state informationthat indicates information being displayed or that is ready for displayby application 236-1, a state queue for enabling the user to go back toa prior state or view of application 236-1, and a redo/undo queue ofprevious actions taken by the user.

Event monitor 271 receives event information from peripherals interface218. Event information includes information about a sub-event (e.g., auser touch on touch-sensitive display 212, as part of a multi-touchgesture). Peripherals interface 218 transmits information it receivesfrom I/O subsystem 206 or a sensor, such as proximity sensor 266,accelerometer(s) 268, and/or microphone 213 (through audio circuitry210). Information that peripherals interface 218 receives from I/Osubsystem 206 includes information from touch-sensitive display 212 or atouch-sensitive surface.

In some embodiments, event monitor 271 sends requests to the peripheralsinterface 218 at predetermined intervals. In response, peripheralsinterface 218 transmits event information. In other embodiments,peripherals interface 218 transmits event information only when there isa significant event (e.g., receiving an input above a predeterminednoise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 270 also includes a hit viewdetermination module 272 and/or an active event recognizer determinationmodule 273.

Hit view determination module 272 provides software procedures fordetermining where a sub-event has taken place within one or more viewswhen touch-sensitive display 212 displays more than one view. Views aremade up of controls and other elements that a user can see on thedisplay.

Another aspect of the user interface associated with an application is aset of views, sometimes herein called application views or userinterface windows, in which information is displayed and touch-basedgestures occur. The application views (of a respective application) inwhich a touch is detected may correspond to programmatic levels within aprogrammatic or view hierarchy of the application. For example, thelowest level view in which a touch is detected may be called the hitview, and the set of events that are recognized as proper inputs may bedetermined based, at least in part, on the hit view of the initial touchthat begins a touch-based gesture.

Hit view determination module 272 receives information related to subevents of a touch-based gesture. When an application has multiple viewsorganized in a hierarchy, hit view determination module 272 identifies ahit view as the lowest view in the hierarchy which should handle thesub-event. In most circumstances, the hit view is the lowest level viewin which an initiating sub-event occurs (e.g., the first sub-event inthe sequence of sub-events that form an event or potential event). Oncethe hit view is identified by the hit view determination module 272, thehit view typically receives all sub-events related to the same touch orinput source for which it was identified as the hit view.

Active event recognizer determination module 273 determines which viewor views within a view hierarchy should receive a particular sequence ofsub-events. In some embodiments, active event recognizer determinationmodule 273 determines that only the hit view should receive a particularsequence of sub-events. In other embodiments, active event recognizerdetermination module 273 determines that all views that include thephysical location of a sub-event are actively involved views andtherefore determines that all actively involved views should receive aparticular sequence of sub-events. In other embodiments, even if touchsub-events were entirely confined to the area associated with oneparticular view, views higher in the hierarchy would still remain asactively involved views.

Event dispatcher module 274 dispatches the event information to an eventrecognizer (e.g., event recognizer 280). In embodiments including activeevent recognizer determination module 273, event dispatcher module 274delivers the event information to an event recognizer determined byactive event recognizer determination module 273. In some embodiments,event dispatcher module 274 stores in an event queue the eventinformation, which is retrieved by a respective event receiver 282.

In some embodiments, operating system 226 includes event sorter 270.Alternatively, application 236-1 includes event sorter 270. In yet otherembodiments, event sorter 270 is a stand-alone module or a part ofanother module stored in memory 202, such as contact/motion module 230.

In some embodiments, application 236-1 includes a plurality of eventhandlers 290 and one or more application views 291, each of whichincludes instructions for handling touch events that occur within arespective view of the application's user interface. Each applicationview 291 of the application 236-1 includes one or more event recognizers280. Typically, a respective application view 291 includes a pluralityof event recognizers 280. In other embodiments, one or more of eventrecognizers 280 are part of a separate module, such as a user interfacekit (not shown) or a higher level object from which application 236-1inherits methods and other properties. In some embodiments, a respectiveevent handler 290 includes one or more of: data updater 276, objectupdater 277, GUI updater 278, and/or event data 279 received from eventsorter 270. Event handler 290 may utilize or call data updater 276,object updater 277, or GUI updater 278 to update the applicationinternal state 292. Alternatively, one or more of the application views291 include one or more respective event handlers 290. Also, in someembodiments, one or more of data updater 276, object updater 277, andGUI updater 278 are included in a respective application view 291.

A respective event recognizer 280 receives event information (e.g.,event data 279) from event sorter 270 and identifies an event from theevent information. Event recognizer 280 includes event receiver 282 andevent comparator 284. In some embodiments, event recognizer 280 alsoincludes at least a subset of: metadata 283 and event deliveryinstructions 288 (which may include sub-event delivery instructions).

Event receiver 282 receives event information from event sorter 270. Theevent information includes information about a sub-event, for example, atouch or a touch movement. Depending on the sub-event, the eventinformation also includes additional information, such as location ofthe sub-event. When the sub-event concerns motion of a touch, the eventinformation may also include speed and direction of the sub-event. Insome embodiments, events include rotation of the device from oneorientation to another (e.g., from a portrait orientation to a landscapeorientation, or vice versa), and the event information includescorresponding information about the current orientation (also calleddevice attitude) of the device.

Event comparator 284 compares the event information to predefined eventor sub-event definitions and, based on the comparison, determines anevent or sub event, or determines or updates the state of an event orsub-event. In some embodiments, event comparator 284 includes eventdefinitions 286. Event definitions 286 contain definitions of events(e.g., predefined sequences of sub-events), for example, event 1(287-1), event 2 (287-2), and others. In some embodiments, sub-events inan event (287) include, for example, touch begin, touch end, touchmovement, touch cancellation, and multiple touching. In one example, thedefinition for event 1 (287-1) is a double tap on a displayed object.The double tap, for example, comprises a first touch (touch begin) onthe displayed object for a predetermined phase, a first liftoff (touchend) for a predetermined phase, a second touch (touch begin) on thedisplayed object for a predetermined phase, and a second liftoff (touchend) for a predetermined phase. In another example, the definition forevent 2 (287-2) is a dragging on a displayed object. The dragging, forexample, comprises a touch (or contact) on the displayed object for apredetermined phase, a movement of the touch across touch-sensitivedisplay 212, and liftoff of the touch (touch end). In some embodiments,the event also includes information for one or more associated eventhandlers 290.

In some embodiments, event definition 287 includes a definition of anevent for a respective user-interface object. In some embodiments, eventcomparator 284 performs a hit test to determine which user-interfaceobject is associated with a sub-event. For example, in an applicationview in which three user-interface objects are displayed ontouch-sensitive display 212, when a touch is detected on touch-sensitivedisplay 212, event comparator 284 performs a hit test to determine whichof the three user-interface objects is associated with the touch(sub-event). If each displayed object is associated with a respectiveevent handler 290, the event comparator uses the result of the hit testto determine which event handler 290 should be activated. For example,event comparator 284 selects an event handler associated with thesub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (287) alsoincludes delayed actions that delay delivery of the event informationuntil after it has been determined whether the sequence of sub-eventsdoes or does not correspond to the event recognizer's event type.

When a respective event recognizer 280 determines that the series ofsub-events do not match any of the events in event definitions 286, therespective event recognizer 280 enters an event impossible, eventfailed, or event ended state, after which it disregards subsequentsub-events of the touch-based gesture. In this situation, other eventrecognizers, if any, that remain active for the hit view continue totrack and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 280 includes metadata283 with configurable properties, flags, and/or lists that indicate howthe event delivery system should perform sub-event delivery to activelyinvolved event recognizers. In some embodiments, metadata 283 includesconfigurable properties, flags, and/or lists that indicate how eventrecognizers may interact, or are enabled to interact, with one another.In some embodiments, metadata 283 includes configurable properties,flags, and/or lists that indicate whether sub-events are delivered tovarying levels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 280 activates eventhandler 290 associated with an event when one or more particularsub-events of an event are recognized. In some embodiments, a respectiveevent recognizer 280 delivers event information associated with theevent to event handler 290. Activating an event handler 290 is distinctfrom sending (and deferred sending) sub-events to a respective hit view.In some embodiments, event recognizer 280 throws a flag associated withthe recognized event, and event handler 290 associated with the flagcatches the flag and performs a predefined process.

In some embodiments, event delivery instructions 288 include sub-eventdelivery instructions that deliver event information about a sub-eventwithout activating an event handler. Instead, the sub-event deliveryinstructions deliver event information to event handlers associated withthe series of sub-events or to actively involved views. Event handlersassociated with the series of sub-events or with actively involved viewsreceive the event information and perform a predetermined process.

In some embodiments, data updater 276 creates and updates data used inapplication 236-1. For example, data updater 276 updates the telephonenumber used in contacts module 237, or stores a video file used in videoplayer module. In some embodiments, object updater 277 creates andupdates objects used in application 236-1. For example, object updater277 creates a new user-interface object or updates the position of auser-interface object. GUI updater 278 updates the GUI. For example, GUIupdater 278 prepares display information and sends it to graphics module232 for display on a touch-sensitive display.

In some embodiments, event handler(s) 290 includes or has access to dataupdater 276, object updater 277, and GUI updater 278. In someembodiments, data updater 276, object updater 277, and GUI updater 278are included in a single module of a respective application 236-1 orapplication view 291. In other embodiments, they are included in two ormore software modules.

It shall be understood that the foregoing discussion regarding eventhandling of user touches on touch-sensitive displays also applies toother forms of user inputs to operate multifunction devices 200 withinput devices, not all of which are initiated on touch screens. Forexample, mouse movement and mouse button presses, optionally coordinatedwith single or multiple keyboard presses or holds; contact movementssuch as taps, drags, scrolls, etc. on touchpads; pen stylus inputs;movement of the device; oral instructions; detected eye movements;biometric inputs; and/or any combination thereof are optionally utilizedas inputs corresponding to sub-events which define an event to berecognized.

FIG. 3 illustrates a portable multifunction device 200 having a touchscreen 212 in accordance with some embodiments. The touch screenoptionally displays one or more graphics within user interface (UI) 300.In this embodiment, as well as others described below, a user is enabledto select one or more of the graphics by making a gesture on thegraphics, for example, with one or more fingers 302 (not drawn to scalein the figure) or one or more styluses 303 (not drawn to scale in thefigure). In some embodiments, selection of one or more graphics occurswhen the user breaks contact with the one or more graphics. In someembodiments, the gesture optionally includes one or more taps, one ormore swipes (from left to right, right to left, upward, and/ordownward), and/or a rolling of a finger (from right to left, left toright, upward, and/or downward) that has made contact with device 200.In some implementations or circumstances, inadvertent contact with agraphic does not select the graphic. For example, a swipe gesture thatsweeps over an application icon optionally does not select thecorresponding application when the gesture corresponding to selection isa tap.

Device 200 may also include one or more physical buttons, such as “home”or menu button 304. As described previously, menu button 304 may be usedto navigate to any application 236 in a set of applications that may beexecuted on device 200. Alternatively, in some embodiments, the menubutton is implemented as a soft key in a GUI displayed on touch screen212.

In one embodiment, device 200 includes touch screen 212, menu button304, push button 306 for powering the device on/off and locking thedevice, volume adjustment button(s) 308, subscriber identity module(SIM) card slot 310, headset jack 312, and docking/charging externalport 224. Push button 306 is, optionally, used to turn the power on/offon the device by depressing the button and holding the button in thedepressed state for a predefined time interval; to lock the device bydepressing the button and releasing the button before the predefinedtime interval has elapsed; and/or to unlock the device or initiate anunlock process. In an alternative embodiment, device 200 also acceptsverbal input for activation or deactivation of some functions throughmicrophone 213. Device 200 also, optionally, includes one or morecontact intensity sensors 265 for detecting intensity of contacts ontouch screen 212 and/or one or more tactile output generators 267 forgenerating tactile outputs for a user of device 200.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments. Device 400 need not be portable. In some embodiments,device 400 is a laptop computer, a desktop computer, a tablet computer,a multimedia player device, a navigation device, an educational device(such as a child's learning toy), a gaming system, or a control device(e.g., a home or industrial controller). Device 400 typically includesone or more processing units (CPUs) 410, one or more network or othercommunications interfaces 460, memory 470, and one or more communicationbuses 420 for interconnecting these components. Communication buses 420optionally include circuitry (sometimes called a chipset) thatinterconnects and controls communications between system components.Device 400 includes input/output (I/O) interface 430 comprising display440, which is typically a touch screen display. I/O interface 430 alsooptionally includes a keyboard and/or mouse (or other pointing device)450 and touchpad 455, tactile output generator 457 for generatingtactile outputs on device 400 (e.g., similar to tactile outputgenerator(s) 267 described above with reference to FIG. 2A), sensors 459(e.g., optical, acceleration, proximity, touch-sensitive, and/or contactintensity sensors similar to contact intensity sensor(s) 265 describedabove with reference to FIG. 2A). Memory 470 includes high-speed randomaccess memory, such as DRAM, SRAM, DDR RAM, or other random access solidstate memory devices; and optionally includes non-volatile memory, suchas one or more magnetic disk storage devices, optical disk storagedevices, flash memory devices, or other non-volatile solid state storagedevices. Memory 470 optionally includes one or more storage devicesremotely located from CPU(s) 410. In some embodiments, memory 470 storesprograms, modules, and data structures analogous to the programs,modules, and data structures stored in memory 202 of portablemultifunction device 200 (FIG. 2A), or a subset thereof. Furthermore,memory 470 optionally stores additional programs, modules, and datastructures not present in memory 202 of portable multifunction device200. For example, memory 470 of device 400 optionally stores drawingmodule 480, presentation module 482, word processing module 484, websitecreation module 486, disk authoring module 488, and/or spreadsheetmodule 490, while memory 202 of portable multifunction device 200 (FIG.2A) optionally does not store these modules.

Each of the above-identified elements in FIG. 4 may be stored in one ormore of the previously mentioned memory devices. Each of theabove-identified modules corresponds to a set of instructions forperforming a function described above. The above-identified modules orprograms (e.g., sets of instructions) need not be implemented asseparate software programs, procedures, or modules, and thus varioussubsets of these modules may be combined or otherwise rearranged invarious embodiments. In some embodiments, memory 470 may store a subsetof the modules and data structures identified above. Furthermore, memory470 may store additional modules and data structures not describedabove.

Attention is now directed towards embodiments of user interfaces thatmay be implemented on, for example, portable multifunction device 200.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on portable multifunction device 200 in accordance withsome embodiments. Similar user interfaces may be implemented on device400. In some embodiments, user interface 500 includes the followingelements, or a subset or superset thereof:

Signal strength indicator(s) 502 for wireless communication(s), such ascellular and Wi-Fi signals;

-   -   Time 504;    -   Bluetooth indicator 505;    -   Battery status indicator 506;    -   Tray 508 with icons for frequently used applications, such as:        -   Icon 516 for telephone module 238, labeled “Phone,” which            optionally includes an indicator 514 of the number of missed            calls or voicemail messages;        -   Icon 518 for email client module 240, labeled “Mail,” which            optionally includes an indicator 510 of the number of unread            emails;        -   Icon 520 for browser module 247, labeled “Browser;” and        -   Icon 522 for video and music player module 252, also            referred to as iPod (trademark of Apple Inc.) module 252,            labeled “iPod;” and    -   Icons for other applications, such as:        -   Icon 524 for IM module 241, labeled “Messages;”        -   Icon 526 for calendar module 248, labeled “Calendar;”        -   Icon 528 for image management module 244, labeled “Photos;”        -   Icon 530 for camera module 243, labeled “Camera;”        -   Icon 532 for online video module 255, labeled “Online            Video;”        -   Icon 534 for stocks widget 249-2, labeled “Stocks;”        -   Icon 536 for map module 254, labeled “Maps;”        -   Icon 538 for weather widget 249-1, labeled “Weather;”        -   Icon 540 for alarm clock widget 249-4, labeled “Clock;”        -   Icon 542 for workout support module 242, labeled “Workout            Support;”        -   Icon 544 for notes module 253, labeled “Notes;” and        -   Icon 546 for a settings application or module, labeled            “Settings,” which provides access to settings for device 200            and its various applications 236.

It should be noted that the icon labels illustrated in FIG. 5A aremerely exemplary. For example, icon 522 for video and music playermodule 252 may optionally be labeled “Music” or “Music Player.” Otherlabels are, optionally, used for various application icons. In someembodiments, a label for a respective application icon includes a nameof an application corresponding to the respective application icon. Insome embodiments, a label for a particular application icon is distinctfrom a name of an application corresponding to the particularapplication icon.

FIG. 5B illustrates an exemplary user interface on a device (e.g.,device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tabletor touchpad 455, FIG. 4) that is separate from the display 550 (e.g.,touch screen display 212). Device 400 also, optionally, includes one ormore contact intensity sensors (e.g., one or more of sensors 457) fordetecting intensity of contacts on touch-sensitive surface 551 and/orone or more tactile output generators 459 for generating tactile outputsfor a user of device 400.

Although some of the examples which follow will be given with referenceto inputs on touch screen display 212 (where the touch-sensitive surfaceand the display are combined), in some embodiments, the device detectsinputs on a touch-sensitive surface that is separate from the display,as shown in FIG. 5B. In some embodiments, the touch-sensitive surface(e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) thatcorresponds to a primary axis (e.g., 553 in FIG. 5B) on the display(e.g., 550). In accordance with these embodiments, the device detectscontacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface551 at locations that correspond to respective locations on the display(e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570).In this way, user inputs (e.g., contacts 560 and 562, and movementsthereof) detected by the device on the touch-sensitive surface (e.g.,551 in FIG. 5B) are used by the device to manipulate the user interfaceon the display (e.g., 550 in FIG. 5B) of the multifunction device whenthe touch-sensitive surface is separate from the display. It should beunderstood that similar methods are, optionally, used for other userinterfaces described herein.

Additionally, while the following examples are given primarily withreference to finger inputs (e.g., finger contacts, finger tap gestures,and finger swipe gestures), it should be understood that, in someembodiments, one or more of the finger inputs are replaced with inputfrom another input device (e.g., a mouse-based input or stylus input).For example, a swipe gesture is, optionally, replaced with a mouse click(e.g., instead of a contact) followed by movement of the cursor alongthe path of the swipe (e.g., instead of movement of the contact). Asanother example, a tap gesture is, optionally, replaced with a mouseclick while the cursor is located over the location of the tap gesture(e.g., instead of detection of the contact followed by ceasing to detectthe contact). Similarly, when multiple user inputs are simultaneouslydetected, it should be understood that multiple computer mice are,optionally, used simultaneously, or a mouse and finger contacts are,optionally, used simultaneously.

FIG. 6A illustrates exemplary personal electronic device 600. Device 600includes body 602. In some embodiments, device 600 can include some orall of the features described with respect to devices 200 and 400 (e.g.,FIGS. 2A-4B). In some embodiments, device 600 has touch-sensitivedisplay screen 604, hereafter touch screen 604. Alternatively, or inaddition to touch screen 604, device 600 has a display and atouch-sensitive surface. As with devices 200 and 400, in someembodiments, touch screen 604 (or the touch-sensitive surface) may haveone or more intensity sensors for detecting intensity of contacts (e.g.,touches) being applied. The one or more intensity sensors of touchscreen 604 (or the touch-sensitive surface) can provide output data thatrepresents the intensity of touches. The user interface of device 600can respond to touches based on their intensity, meaning that touches ofdifferent intensities can invoke different user interface operations ondevice 600.

Techniques for detecting and processing touch intensity may be found,for example, in related applications: International Patent ApplicationSerial No. PCT/US2013/040061, titled “Device, Method, and Graphical UserInterface for Displaying User Interface Objects Corresponding to anApplication,” filed May 8, 2013, and International Patent ApplicationSerial No. PCT/US2013/069483, titled “Device, Method, and Graphical UserInterface for Transitioning Between Touch Input to Display OutputRelationships,” filed Nov. 11, 2013, each of which is herebyincorporated by reference in their entirety.

In some embodiments, device 600 has one or more input mechanisms 606 and608. Input mechanisms 606 and 608, if included, can be physical.Examples of physical input mechanisms include push buttons and rotatablemechanisms. In some embodiments, device 600 has one or more attachmentmechanisms. Such attachment mechanisms, if included, can permitattachment of device 600 with, for example, hats, eyewear, earrings,necklaces, shirts, jackets, bracelets, watch straps, chains, trousers,belts, shoes, purses, backpacks, and so forth. These attachmentmechanisms may permit device 600 to be worn by a user.

FIG. 6B depicts exemplary personal electronic device 600. In someembodiments, device 600 can include some or all of the componentsdescribed with respect to FIGS. 2A, 2B, and 4. Device 600 has bus 612that operatively couples I/O section 614 with one or more computerprocessors 616 and memory 618. I/O section 614 can be connected todisplay 604, which can have touch-sensitive component 622 and,optionally, touch-intensity sensitive component 624. In addition, I/Osection 614 can be connected with communication unit 630 for receivingapplication and operating system data using Wi-Fi, Bluetooth, near fieldcommunication (NFC), cellular, and/or other wireless communicationtechniques. Device 600 can include input mechanisms 606 and/or 608.Input mechanism 606 may be a rotatable input device or a depressible androtatable input device, for example. Input mechanism 608 may be abutton, in some examples.

Input mechanism 608 may be a microphone, in some examples. Personalelectronic device 600 can include various sensors, such as GPS sensor632, accelerometer 634, directional sensor 640 (e.g., compass),gyroscope 636, motion sensor 638, and/or a combination thereof, all ofwhich can be operatively connected to I/O section 614.

Memory 618 of personal electronic device 600 can be a non-transitorycomputer-readable storage medium for storing computer-executableinstructions, which, when executed by one or more computer processors616, for example, can cause the computer processors to perform thetechniques described below, including process 1200 (FIGS. 12A-D). Thecomputer-executable instructions can also be stored and/or transportedwithin any non-transitory computer-readable storage medium for use by orin connection with an instruction execution system, apparatus, ordevice, such as a computer-based system, processor-containing system, orother system that can fetch the instructions from the instructionexecution system, apparatus, or device and execute the instructions.Personal electronic device 600 is not limited to the components andconfiguration of FIG. 6B, but can include other or additional componentsin multiple configurations.

As used here, the term “affordance” refers to a user-interactivegraphical user interface object that may be displayed on the displayscreen of devices 200, 400, and/or 600 (FIGS. 2, 4, and 6). For example,an image (e.g., icon), a button, and text (e.g., link) may eachconstitute an affordance.

As used herein, the term “focus selector” refers to an input elementthat indicates a current part of a user interface with which a user isinteracting. In some implementations that include a cursor or otherlocation marker, the cursor acts as a “focus selector” so that when aninput (e.g., a press input) is detected on a touch-sensitive surface(e.g., touchpad 455 in FIG. 4 or touch-sensitive surface 551 in FIG. 5B)while the cursor is over a particular user interface element (e.g., abutton, window, slider or other user interface element), the particularuser interface element is adjusted in accordance with the detectedinput. In some implementations that include a touch screen display(e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212in FIG. 5A) that enables direct interaction with user interface elementson the touch screen display, a detected contact on the touch screen actsas a “focus selector” so that when an input (e.g., a press input by thecontact) is detected on the touch screen display at a location of aparticular user interface element (e.g., a button, window, slider, orother user interface element), the particular user interface element isadjusted in accordance with the detected input. In some implementations,focus is moved from one region of a user interface to another region ofthe user interface without corresponding movement of a cursor ormovement of a contact on a touch screen display (e.g., by using a tabkey or arrow keys to move focus from one button to another button); inthese implementations, the focus selector moves in accordance withmovement of focus between different regions of the user interface.Without regard to the specific form taken by the focus selector, thefocus selector is generally the user interface element (or contact on atouch screen display) that is controlled by the user so as tocommunicate the user's intended interaction with the user interface(e.g., by indicating, to the device, the element of the user interfacewith which the user is intending to interact). For example, the locationof a focus selector (e.g., a cursor, a contact, or a selection box) overa respective button while a press input is detected on thetouch-sensitive surface (e.g., a touchpad or touch screen) will indicatethat the user is intending to activate the respective button (as opposedto other user interface elements shown on a display of the device).

As used in the specification and claims, the term “characteristicintensity” of a contact refers to a characteristic of the contact basedon one or more intensities of the contact. In some embodiments, thecharacteristic intensity is based on multiple intensity samples. Thecharacteristic intensity is, optionally, based on a predefined number ofintensity samples, or a set of intensity samples collected during apredetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10seconds) relative to a predefined event (e.g., after detecting thecontact, prior to detecting liftoff of the contact, before or afterdetecting a start of movement of the contact, prior to detecting an endof the contact, before or after detecting an increase in intensity ofthe contact, and/or before or after detecting a decrease in intensity ofthe contact). A characteristic intensity of a contact is, optionallybased on one or more of: a maximum value of the intensities of thecontact, a mean value of the intensities of the contact, an averagevalue of the intensities of the contact, a top 10 percentile value ofthe intensities of the contact, a value at the half maximum of theintensities of the contact, a value at the 90 percent maximum of theintensities of the contact, or the like. In some embodiments, theduration of the contact is used in determining the characteristicintensity (e.g., when the characteristic intensity is an average of theintensity of the contact over time). In some embodiments, thecharacteristic intensity is compared to a set of one or more intensitythresholds to determine whether an operation has been performed by auser. For example, the set of one or more intensity thresholds mayinclude a first intensity threshold and a second intensity threshold. Inthis example, a contact with a characteristic intensity that does notexceed the first threshold results in a first operation, a contact witha characteristic intensity that exceeds the first intensity thresholdand does not exceed the second intensity threshold results in a secondoperation, and a contact with a characteristic intensity that exceedsthe second threshold results in a third operation. In some embodiments,a comparison between the characteristic intensity and one or morethresholds is used to determine whether or not to perform one or moreoperations (e.g., whether to perform a respective operation or forgoperforming the respective operation) rather than being used to determinewhether to perform a first operation or a second operation.

In some embodiments, a portion of a gesture is identified for purposesof determining a characteristic intensity. For example, atouch-sensitive surface may receive a continuous swipe contacttransitioning from a start location and reaching an end location, atwhich point the intensity of the contact increases. In this example, thecharacteristic intensity of the contact at the end location may be basedon only a portion of the continuous swipe contact, and not the entireswipe contact (e.g., only the portion of the swipe contact at the endlocation). In some embodiments, a smoothing algorithm may be applied tothe intensities of the swipe contact prior to determining thecharacteristic intensity of the contact. For example, the smoothingalgorithm optionally includes one or more of: an unweightedsliding-average smoothing algorithm, a triangular smoothing algorithm, amedian filter smoothing algorithm, and/or an exponential smoothingalgorithm. In some circumstances, these smoothing algorithms eliminatenarrow spikes or dips in the intensities of the swipe contact forpurposes of determining a characteristic intensity.

The intensity of a contact on the touch-sensitive surface may becharacterized relative to one or more intensity thresholds, such as acontact-detection intensity threshold, a light press intensitythreshold, a deep press intensity threshold, and/or one or more otherintensity thresholds. In some embodiments, the light press intensitythreshold corresponds to an intensity at which the device will performoperations typically associated with clicking a button of a physicalmouse or a trackpad. In some embodiments, the deep press intensitythreshold corresponds to an intensity at which the device will performoperations that are different from operations typically associated withclicking a button of a physical mouse or a trackpad. In someembodiments, when a contact is detected with a characteristic intensitybelow the light press intensity threshold (e.g., and above a nominalcontact-detection intensity threshold below which the contact is nolonger detected), the device will move a focus selector in accordancewith movement of the contact on the touch-sensitive surface withoutperforming an operation associated with the light press intensitythreshold or the deep press intensity threshold. Generally, unlessotherwise stated, these intensity thresholds are consistent betweendifferent sets of user interface figures.

An increase of characteristic intensity of the contact from an intensitybelow the light press intensity threshold to an intensity between thelight press intensity threshold and the deep press intensity thresholdis sometimes referred to as a “light press” input. An increase ofcharacteristic intensity of the contact from an intensity below the deeppress intensity threshold to an intensity above the deep press intensitythreshold is sometimes referred to as a “deep press” input. An increaseof characteristic intensity of the contact from an intensity below thecontact-detection intensity threshold to an intensity between thecontact-detection intensity threshold and the light press intensitythreshold is sometimes referred to as detecting the contact on thetouch-surface. A decrease of characteristic intensity of the contactfrom an intensity above the contact-detection intensity threshold to anintensity below the contact-detection intensity threshold is sometimesreferred to as detecting liftoff of the contact from the touch-surface.In some embodiments, the contact-detection intensity threshold is zero.In some embodiments, the contact-detection intensity threshold isgreater than zero.

In some embodiments described herein, one or more operations areperformed in response to detecting a gesture that includes a respectivepress input or in response to detecting the respective press inputperformed with a respective contact (or a plurality of contacts), wherethe respective press input is detected based at least in part ondetecting an increase in intensity of the contact (or plurality ofcontacts) above a press-input intensity threshold. In some embodiments,the respective operation is performed in response to detecting theincrease in intensity of the respective contact above the press-inputintensity threshold (e.g., a “down stroke” of the respective pressinput). In some embodiments, the press input includes an increase inintensity of the respective contact above the press-input intensitythreshold and a subsequent decrease in intensity of the contact belowthe press-input intensity threshold, and the respective operation isperformed in response to detecting the subsequent decrease in intensityof the respective contact below the press-input threshold (e.g., an “upstroke” of the respective press input).

In some embodiments, the device employs intensity hysteresis to avoidaccidental inputs, sometimes termed “jitter,” where the device definesor selects a hysteresis intensity threshold with a predefinedrelationship to the press-input intensity threshold (e.g., thehysteresis intensity threshold is X intensity units lower than thepress-input intensity threshold or the hysteresis intensity threshold is75%, 90%, or some reasonable proportion of the press-input intensitythreshold). Thus, in some embodiments, the press input includes anincrease in intensity of the respective contact above the press-inputintensity threshold and a subsequent decrease in intensity of thecontact below the hysteresis intensity threshold that corresponds to thepress-input intensity threshold, and the respective operation isperformed in response to detecting the subsequent decrease in intensityof the respective contact below the hysteresis intensity threshold(e.g., an “up stroke” of the respective press input). Similarly, in someembodiments, the press input is detected only when the device detects anincrease in intensity of the contact from an intensity at or below thehysteresis intensity threshold to an intensity at or above thepress-input intensity threshold and, optionally, a subsequent decreasein intensity of the contact to an intensity at or below the hysteresisintensity, and the respective operation is performed in response todetecting the press input (e.g., the increase in intensity of thecontact or the decrease in intensity of the contact, depending on thecircumstances).

For ease of explanation, the descriptions of operations performed inresponse to a press input associated with a press-input intensitythreshold or in response to a gesture including the press input are,optionally, triggered in response to detecting either: an increase inintensity of a contact above the press-input intensity threshold, anincrease in intensity of a contact from an intensity below thehysteresis intensity threshold to an intensity above the press-inputintensity threshold, a decrease in intensity of the contact below thepress-input intensity threshold, and/or a decrease in intensity of thecontact below the hysteresis intensity threshold corresponding to thepress-input intensity threshold. Additionally, in examples where anoperation is described as being performed in response to detecting adecrease in intensity of a contact below the press-input intensitythreshold, the operation is, optionally, performed in response todetecting a decrease in intensity of the contact below a hysteresisintensity threshold corresponding to, and lower than, the press-inputintensity threshold.

3. Digital Assistant System

FIG. 7A illustrates a block diagram of digital assistant system 700 inaccordance with various examples. In some examples, digital assistantsystem 700 can be implemented on a standalone computer system. In someexamples, digital assistant system 700 can be distributed acrossmultiple computers. In some examples, some of the modules and functionsof the digital assistant can be divided into a server portion and aclient portion, where the client portion resides on one or more userdevices (e.g., devices 104, 122, 200, 400, or 600) and communicates withthe server portion (e.g., server system 108) through one or morenetworks, e.g., as shown in FIG. 1. In some examples, digital assistantsystem 700 can be an implementation of server system 108 (and/or DAserver 106) shown in FIG. 1. It should be noted that digital assistantsystem 700 is only one example of a digital assistant system, and thatdigital assistant system 700 can have more or fewer components thanshown, may combine two or more components, or may have a differentconfiguration or arrangement of the components. The various componentsshown in FIG. 7A can be implemented in hardware, software instructionsfor execution by one or more processors, firmware including one or moresignal processing and/or application specific integrated circuits, or acombination thereof.

Digital assistant system 700 can include memory 702, one or moreprocessors 704, input/output (I/O) interface 706, and networkcommunications interface 708. These components can communicate with oneanother over one or more communication buses or signal lines 710.

In some examples, memory 702 can include a non-transitorycomputer-readable medium, such as high-speed random access memory and/ora non-volatile computer-readable storage medium (e.g., one or moremagnetic disk storage devices, flash memory devices, or othernon-volatile solid-state memory devices).

In some examples, I/O interface 706 can couple input/output devices 716of digital assistant system 700, such as displays, keyboards, touchscreens, and microphones, to user interface module 722. I/O interface706, in conjunction with user interface module 722, can receive userinputs (e.g., voice input, keyboard inputs, touch inputs, etc.) andprocess them accordingly. In some examples, e.g., when the digitalassistant is implemented on a standalone user device, digital assistantsystem 700 can include any of the components and I/O communicationinterfaces described with respect to devices 200, 400, or 600 in FIGS.2A, 4, 6A-B, respectively. In some examples, digital assistant system700 can represent the server portion of a digital assistantimplementation and can interact with the user through a client-sideportion residing on a user device (e.g., devices 104, 200, 400, or 600).

In some examples, the network communications interface 708 can includewired communication port(s) 712 and/or wireless transmission andreception circuitry 714. The wired communication port(s) 712 can receiveand send communication signals via one or more wired interfaces, e.g.,Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wirelesscircuitry 714 can receive and send RF signals and/or optical signalsfrom/to communications networks and other communications devices. Thewireless communications can use any of a plurality of communicationsstandards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA,Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communicationprotocol. Network communications interface 708 can enable communicationbetween digital assistant system 700 with networks, such as theInternet, an intranet, and/or a wireless network, such as a cellulartelephone network, a wireless local area network (LAN), and/or ametropolitan area network (MAN), and other devices.

In some examples, memory 702, or the computer-readable storage media ofmemory 702, can store programs, modules, instructions, and datastructures including all or a subset of: operating system 718,communications module 720, user interface module 722, one or moreapplications 724, and digital assistant module 726. In particular,memory 702, or the computer-readable storage media of memory 702, canstore instructions for performing process 1200, described below. One ormore processors 704 can execute these programs, modules, andinstructions, and read/write from/to the data structures.

Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X,WINDOWS, or an embedded operating system such as VxWorks) can includevarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communications between varioushardware, firmware, and software components.

Communications module 720 can facilitate communications between digitalassistant system 700 with other devices over network communicationsinterface 708. For example, communications module 720 can communicatewith RF circuitry 208 of electronic devices such as devices 200, 400,and 600 shown in FIGS. 2A, 4, 6A-B, respectively. Communications module720 can also include various components for handling data received bywireless circuitry 714 and/or wired communications port 712.

User interface module 722 can receive commands and/or inputs from a uservia I/O interface 706 (e.g., from a keyboard, touch screen, pointingdevice, controller, and/or microphone), and generate user interfaceobjects on a display. User interface module 722 can also prepare anddeliver outputs (e.g., speech, sound, animation, text, icons,vibrations, haptic feedback, light, etc.) to the user via the I/Ointerface 706 (e.g., through displays, audio channels, speakers,touch-pads, etc.).

Applications 724 can include programs and/or modules that are configuredto be executed by one or more processors 704. For example, if thedigital assistant system is implemented on a standalone user device,applications 724 can include user applications, such as games, acalendar application, a navigation application, or an email application.If digital assistant system 700 is implemented on a server, applications724 can include resource management applications, diagnosticapplications, or scheduling applications, for example.

Memory 702 can also store digital assistant module 726 (or the serverportion of a digital assistant). In some examples, digital assistantmodule 726 can include the following sub-modules, or a subset orsuperset thereof: input/output processing module 728, speech-to-text(STT) processing module 730, natural language processing module 732,dialogue flow processing module 734, task flow processing module 736,service processing module 738, and speech synthesis module 740. Each ofthese modules can have access to one or more of the following systems ordata and models of the digital assistant module 726, or a subset orsuperset thereof: ontology 760, vocabulary index 744, user data 748,task flow models 754, service models 756, and ASR systems 731.

In some examples, using the processing modules, data, and modelsimplemented in digital assistant module 726, the digital assistant canperform at least some of the following: converting speech input intotext; identifying a user's intent expressed in a natural language inputreceived from the user; actively eliciting and obtaining informationneeded to fully infer the user's intent (e.g., by disambiguating words,games, intentions, etc.); determining the task flow for fulfilling theinferred intent; and executing the task flow to fulfill the inferredintent.

In some examples, as shown in FIG. 7B, I/O processing module 728 caninteract with the user through I/O devices 716 in FIG. 7A or with a userdevice (e.g., devices 104, 200, 400, or 600) through networkcommunications interface 708 in FIG. 7A to obtain user input (e.g., aspeech input) and to provide responses (e.g., as speech outputs) to theuser input. I/O processing module 728 can optionally obtain contextualinformation associated with the user input from the user device, alongwith or shortly after the receipt of the user input. The contextualinformation can include user-specific data, vocabulary, and/orpreferences relevant to the user input. In some examples, the contextualinformation also includes software and hardware states of the userdevice at the time the user request is received, and/or informationrelated to the surrounding environment of the user at the time that theuser request is received. In some examples, I/O processing module 728can also send follow-up questions to, and receive answers from, the userregarding the user request. When a user request is received by I/Oprocessing module 728 and the user request can include speech input, I/Oprocessing module 728 can forward the speech input to STT processingmodule 730 (or speech recognizer) for speech-to-text conversions.

STT processing module 730 can include one or more ASR systems. The oneor more ASR systems can process the speech input that is receivedthrough I/O processing module 728 to produce a recognition result. EachASR system can include a front-end speech pre-processor. The front-endspeech pre-processor can extract representative features from the speechinput. For example, the front-end speech pre-processor can perform aFourier transform on the speech input to extract spectral features thatcharacterize the speech input as a sequence of representativemulti-dimensional vectors. Further, each ASR system can include one ormore speech recognition models (e.g., acoustic models and/or languagemodels) and can implement one or more speech recognition engines.Examples of speech recognition models can include Hidden Markov Models,Gaussian-Mixture Models, Deep Neural Network Models, n-gram languagemodels, and other statistical models. Examples of speech recognitionengines can include the dynamic time warping based engines and weightedfinite-state transducers (WFST) based engines. The one or more speechrecognition models and the one or more speech recognition engines can beused to process the extracted representative features of the front-endspeech pre-processor to produce intermediate recognitions results (e.g.,phonemes, phonemic strings, and sub-words), and ultimately, textrecognition results (e.g., words, word strings, or sequence of tokens).In some examples, the speech input can be processed at least partiallyby a third-party service or on the user's device (e.g., device 104, 200,400, or 600) to produce the recognition result. Once STT processingmodule 730 produces recognition results containing a text string (e.g.,words, or sequence of words, or sequence of tokens), the recognitionresult can be passed to natural language processing module 732 forintent deduction.

More details on the speech-to-text processing are described in U.S.Utility application Ser. No. 13/236,942 for “Consolidating SpeechRecognition Results,” filed on Sep. 20, 2011, the entire disclosure ofwhich is incorporated herein by reference.

In some examples, STT processing module 730 can include and/or access avocabulary of recognizable words via phonetic alphabet conversion module731. Each vocabulary word can be associated with one or more candidatepronunciations of the word represented in a speech recognition phoneticalphabet. In particular, the vocabulary of recognizable words caninclude a word that is associated with a plurality of candidatepronunciations. For example, the vocabulary may include the word“tomato” that is associated with the candidate pronunciations of /

/ and /

/ . Further, vocabulary words can be associated with custom candidatepronunciations that are based on previous speech inputs from the user.Such custom candidate pronunciations can be stored in STT processingmodule 730 and can be associated with a particular user via the user'sprofile on the device. In some examples, the candidate pronunciationsfor words can be determined based on the spelling of the word and one ormore linguistic and/or phonetic rules. In some examples, the candidatepronunciations can be manually generated, e.g., based on known canonicalpronunciations.

In some examples, the candidate pronunciations can be ranked based onthe commonness of the candidate pronunciation. For example, thecandidate pronunciation /

/ can be ranked higher than /

/, because the former is a more commonly used pronunciation (e.g., amongall users, for users in a particular geographical region, or for anyother appropriate subset of users). In some examples, candidatepronunciations can be ranked based on whether the candidatepronunciation is a custom candidate pronunciation associated with theuser. For example, custom candidate pronunciations can be ranked higherthan canonical candidate pronunciations. This can be useful forrecognizing proper nouns having a unique pronunciation that deviatesfrom canonical pronunciation. In some examples, candidate pronunciationscan be associated with one or more speech characteristics, such asgeographic origin, nationality, or ethnicity. For example, the candidatepronunciation /

/ can be associated with the United States, whereas the candidatepronunciation /

/ can be associated with Great Britain. Further, the rank of thecandidate pronunciation can be based on one or more characteristics(e.g., geographic origin, nationality, ethnicity, etc.) of the userstored in the user's profile on the device. For example, it can bedetermined from the user's profile that the user is associated with theUnited States. Based on the user being associated with the UnitedStates, the candidate pronunciation /

/ (associated with the United States) can be ranked higher than thecandidate pronunciation /

/ (associated with Great Britain). In some examples, one of the rankedcandidate pronunciations can be selected as a predicted pronunciation(e.g., the most likely pronunciation).

When a speech input is received, STT processing module 730 can be usedto determine the phonemes corresponding to the speech input (e.g., usingan acoustic model) and then attempt to determine words that match thephonemes (e.g., using a language model). For example, if STT processingmodule 730 can first identify the sequence of phonemes /

/ corresponding to a portion of the speech input, it can then determine,based on vocabulary index 744, that this sequence corresponds to theword “tomato.”

In some examples, STT processing module 730 can use approximate matchingtechniques to determine words in a voice input. Thus, for example, theSTT processing module 730 can determine that the sequence of phonemes /

/ corresponds to the word “tomato,” even if that particular sequence ofphonemes is not one of the candidate sequence of phonemes for that word.

Natural language processing module 732 (“natural language processor”) ofthe digital assistant can take the sequence of words or tokens (“tokensequence”) generated by STT processing module 730 and attempt toassociate the token sequence with one or more “actionable intents”recognized by the digital assistant. An “actionable intent” canrepresent a task that can be performed by the digital assistant and canhave an associated task flow implemented in task flow models 754. Theassociated task flow can be a series of programmed actions and stepsthat the digital assistant takes in order to perform the task. The scopeof a digital assistant's capabilities can be dependent on the number andvariety of task flows that have been implemented and stored in task flowmodels 754 or, in other words, on the number and variety of “actionableintents” that the digital assistant recognizes. The effectiveness of thedigital assistant, however, can also be dependent on the assistant'sability to infer the correct “actionable intent(s)” from the userrequest expressed in natural language.

In some examples, in addition to the sequence of words or tokensobtained from STT processing module 730, natural language processingmodule 732 can also receive contextual information associated with theuser request, e.g., from I/O processing module 728. The natural languageprocessing module 732 can optionally use the contextual information toclarify, supplement, and/or further define the information contained inthe token sequence received from STT processing module 730. Thecontextual information can include, for example, user preferences,hardware, and/or software states of the user device, sensor informationcollected before, during, or shortly after the user request, priorinteractions (e.g., dialogue) between the digital assistant and theuser, and the like. As described herein, contextual information can bedynamic and can change with time, location, content of the dialogue, andother factors.

In some examples, the natural language processing can be based on, e.g.,ontology 760. Ontology 760 can be a hierarchical structure containingmany nodes, each node representing either an “actionable intent” or a“property” relevant to one or more of the “actionable intents” or other“properties.” As noted above, an “actionable intent” can represent atask that the digital assistant is capable of performing, i.e., it is“actionable” or can be acted on. A “property” can represent a parameterassociated with an actionable intent or a sub-aspect of anotherproperty. A linkage between an actionable intent node and a propertynode in ontology 760 can define how a parameter represented by theproperty node pertains to the task represented by the actionable intentnode.

In some examples, ontology 760 can be made up of actionable intent nodesand property nodes. Within ontology 760, each actionable intent node canbe linked to one or more property nodes either directly or through oneor more intermediate property nodes. Similarly, each property node canbe linked to one or more actionable intent nodes either directly orthrough one or more intermediate property nodes. For example, as shownin FIG. 7C, ontology 760 can include a “restaurant reservation” node(i.e., an actionable intent node). Property nodes “restaurant,”“date/time” (for the reservation), and “party size” can each be directlylinked to the actionable intent node (i.e., the “restaurant reservation”node).

In addition, property nodes “cuisine,” “price range,” “phone number,”and “location” can be sub-nodes of the property node “restaurant,” andcan each be linked to the “restaurant reservation” node (i.e., theactionable intent node) through the intermediate property node“restaurant.” For another example, as shown in FIG. 7C, ontology 760 canalso include a “set reminder” node (i.e., another actionable intentnode). Property nodes “date/time” (for setting the reminder) and“subject” (for the reminder) can each be linked to the “set reminder”node. Since the property “date/time” can be relevant to both the task ofmaking a restaurant reservation and the task of setting a reminder, theproperty node “date/time” can be linked to both the “restaurantreservation” node and the “set reminder” node in ontology 760.

An actionable intent node, along with its linked concept nodes, can bedescribed as a “domain.” In the present discussion, each domain can beassociated with a respective actionable intent and refers to the groupof nodes (and the relationships there between) associated with theparticular actionable intent. For example, ontology 760 shown in FIG. 7Ccan include an example of restaurant reservation domain 762 and anexample of reminder domain 764 within ontology 760. The restaurantreservation domain includes the actionable intent node “restaurantreservation,” property nodes “restaurant,” “date/time,” and “partysize,” and sub-property nodes “cuisine,” “price range,” “phone number,”and “location.” Reminder domain 764 can include the actionable intentnode “set reminder,” and property nodes “subject” and “date/time.” Insome examples, ontology 760 can be made up of many domains. Each domaincan share one or more property nodes with one or more other domains. Forexample, the “date/time” property node can be associated with manydifferent domains (e.g., a scheduling domain, a travel reservationdomain, a movie ticket domain, etc.), in addition to restaurantreservation domain 762 and reminder domain 764.

While FIG. 7C illustrates two example domains within ontology 760, otherdomains can include, for example, “find a movie,” “initiate a phonecall,” “find directions,” “schedule a meeting,” “send a message,” and“provide an answer to a question,” “read a list,” “providing navigationinstructions,” “provide instructions for a task,” and so on. A “send amessage” domain can be associated with a “send a message” actionableintent node, and may further include property nodes such as“recipient(s),” “message type,” and “message body.” The property node“recipient” can be further defined, for example, by the sub-propertynodes such as “recipient name” and “message address.”

In some examples, ontology 760 can include all the domains (and henceactionable intents) that the digital assistant is capable ofunderstanding and acting upon. In some examples, ontology 760 can bemodified, such as by adding or removing entire domains or nodes, or bymodifying relationships between the nodes within the ontology 760.

In some examples, nodes associated with multiple related actionableintents can be clustered under a “super domain” in ontology 760. Forexample, a “travel” super-domain can include a cluster of property nodesand actionable intent nodes related to travel. The actionable intentnodes related to travel can include “airline reservation,” “hotelreservation,” “car rental,” “get directions,” “find points of interest,”and so on. The actionable intent nodes under the same super domain(e.g., the “travel” super domain) can have many property nodes incommon. For example, the actionable intent nodes for “airlinereservation,” “hotel reservation,” “car rental,” “get directions,” and“find points of interest” can share one or more of the property nodes“start location,” “destination,” “departure date/time,” “arrivaldate/time,” and “party size.”

In some examples, each node in ontology 760 can be associated with a setof words and/or phrases that are relevant to the property or actionableintent represented by the node. The respective set of words and/orphrases associated with each node can be the so-called “vocabulary”associated with the node. The respective set of words and/or phrasesassociated with each node can be stored in vocabulary index 744 inassociation with the property or actionable intent represented by thenode. For example, returning to FIG. 7B, the vocabulary associated withthe node for the property of “restaurant” can include words such as“food,” “drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,”“meal,” and so on. For another example, the vocabulary associated withthe node for the actionable intent of “initiate a phone call” caninclude words and phrases such as “call,” “phone,” “dial,” “ring,” “callthis number,” “make a call to,” and so on. The vocabulary index 744 canoptionally include words and phrases in different languages.

Natural language processing module 732 can receive the token sequence(e.g., a text string) from STT processing module 730 and determine whatnodes are implicated by the words in the token sequence. In someexamples, if a word or phrase in the token sequence is found to beassociated with one or more nodes in ontology 760 (via vocabulary index744), the word or phrase can “trigger” or “activate” those nodes. Basedon the quantity and/or relative importance of the activated nodes,natural language processing module 732 can select one of the actionableintents as the task that the user intended the digital assistant toperform. In some examples, the domain that has the most “triggered”nodes can be selected. In some examples, the domain having the highestconfidence value (e.g., based on the relative importance of its varioustriggered nodes) can be selected. In some examples, the domain can beselected based on a combination of the number and the importance of thetriggered nodes. In some examples, additional factors are considered inselecting the node as well, such as whether the digital assistant haspreviously correctly interpreted a similar request from a user.

User data 748 can include user-specific information, such asuser-specific vocabulary, user preferences, user address, user's defaultand secondary languages, user's contact list, and other short-term orlong-term information for each user. In some examples, natural languageprocessing module 732 can use the user-specific information tosupplement the information contained in the user input to further definethe user intent. For example, for a user request “invite my friends tomy birthday party,” natural language processing module 732 can be ableto access user data 748 to determine who the “friends” are and when andwhere the “birthday party” would be held, rather than requiring the userto provide such information explicitly in his/her request.

Other details of searching an ontology based on a token string isdescribed in U.S. Utility application Ser. No. 12/341,743 for “Methodand Apparatus for Searching Using An Active Ontology,” filed Dec. 22,2008, the entire disclosure of which is incorporated herein byreference.

In some examples, once natural language processing module 732 identifiesan actionable intent (or domain) based on the user request, naturallanguage processing module 732 can generate a structured query torepresent the identified actionable intent. In some examples, thestructured query can include parameters for one or more nodes within thedomain for the actionable intent, and at least some of the parametersare populated with the specific information and requirements specifiedin the user request. For example, the user may say “Make me a dinnerreservation at a sushi place at 7.” In this case, natural languageprocessing module 732 can be able to correctly identify the actionableintent to be “restaurant reservation” based on the user input. Accordingto the ontology, a structured query for a “restaurant reservation”domain may include parameters such as {Cuisine}, {Time}, {Date}, {PartySize}, and the like. In some examples, based on the speech input and thetext derived from the speech input using STT processing module 730,natural language processing module 732 can generate a partial structuredquery for the restaurant reservation domain, where the partialstructured query includes the parameters {Cuisine =“Sushi”} and {Time=“7pm”}. However, in this example, the user's speech input containsinsufficient information to complete the structured query associatedwith the domain. Therefore, other necessary parameters such as {PartySize} and {Date} may not be specified in the structured query based onthe information currently available. In some examples, natural languageprocessing module 732 can populate some parameters of the structuredquery with received contextual information. For example, in someexamples, if the user requested a sushi restaurant “near me,” naturallanguage processing module 732 can populate a {location} parameter inthe structured query with GPS coordinates from the user device.

In some examples, natural language processing module 732 can pass thegenerated structured query (including any completed parameters) to taskflow processing module 736 (“task flow processor”). Task flow processingmodule 736 can be configured to receive the structured query fromnatural language processing module 732, complete the structured query,if necessary, and perform the actions required to “complete” the user'sultimate request. In some examples, the various procedures necessary tocomplete these tasks can be provided in task flow models 754. In someexamples, task flow models 754 can include procedures for obtainingadditional information from the user and task flows for performingactions associated with the actionable intent.

As described above, in order to complete a structured query, task flowprocessing module 736 may need to initiate additional dialogue with theuser in order to obtain additional information, and/or disambiguatepotentially ambiguous speech inputs. When such interactions arenecessary, task flow processing module 736 can invoke dialogue flowprocessing module 734 to engage in a dialogue with the user. In someexamples, dialogue flow processing module 734 can determine how (and/orwhen) to ask the user for the additional information and receive andprocess the user responses. The questions can be provided to and answerscan be received from the users through I/O processing module 728. Insome examples, dialogue flow processing module 734 can present dialogueoutput to the user via audio and/or visual output and receive input fromthe user via spoken or physical (e.g., clicking) responses. Continuingwith the example above, when task flow processing module 736 invokesdialogue flow processing module 734 to determine the “party size” and“date” information for the structured query associated with the domain“restaurant reservation,” dialogue flow processing module 734 cangenerate questions such as “For how many people?” and “On which day?” topass to the user. Once answers are received from the user, dialogue flowprocessing module 734 can then populate the structured query with themissing information or pass the information to task flow processingmodule 736 to complete the missing information from the structuredquery.

Once task flow processing module 736 has completed the structured queryfor an actionable intent, task flow processing module 736 can proceed toperform the ultimate task associated with the actionable intent.Accordingly, task flow processing module 736 can execute the steps andinstructions in the task flow model according to the specific parameterscontained in the structured query. For example, the task flow model forthe actionable intent of “restaurant reservation” can include steps andinstructions for contacting a restaurant and actually requesting areservation for a particular party size at a particular time. Forexample, using a structured query such as: {restaurant reservation,restaurant=ABC Café, date=Mar. 12, 2012, time=7 pm, party size=5}, taskflow processing module 736 can perform the steps of: (1) logging onto aserver of the ABC Café or a restaurant reservation system such asOPENTABLE®, (2) entering the date, time, and party size information in aform on the website, (3) submitting the form, and (4) making a calendarentry for the reservation in the user's calendar.

In some examples, task flow processing module 736 can employ theassistance of service processing module 738 (“service processingmodule”) to complete a task requested in the user input or to provide aninformational answer requested in the user input. For example, serviceprocessing module 738 can act on behalf of task flow processing module736 to make a phone call, set a calendar entry, invoke a map search,invoke or interact with other user applications installed on the userdevice, and invoke or interact with third-party services (e.g., arestaurant reservation portal, a social networking website, a bankingportal, etc.). In some examples, the protocols and applicationprogramming interfaces (API) required by each service can be specifiedby a respective service model among service models 756. Serviceprocessing module 738 can access the appropriate service model for aservice and generate requests for the service in accordance with theprotocols and APIs required by the service according to the servicemodel.

For example, if a restaurant has enabled an online reservation service,the restaurant can submit a service model specifying the necessaryparameters for making a reservation and the APIs for communicating thevalues of the necessary parameter to the online reservation service.When requested by task flow processing module 736, service processingmodule 738 can establish a network connection with the onlinereservation service using the web address stored in the service modeland send the necessary parameters of the reservation (e.g., time, date,party size) to the online reservation interface in a format according tothe API of the online reservation service.

In some examples, natural language processing module 732, dialogue flowprocessing module 734, and task flow processing module 736 can be usedcollectively and iteratively to infer and define the user's intent,obtain information to further clarify and refine the user intent, andfinally generate a response (i.e., an output to the user, or thecompletion of a task) to fulfill the user's intent. The generatedresponse can be a dialogue response to the speech input that at leastpartially fulfills the user's intent. Further, in some examples, thegenerated response can be output as a speech output. In these examples,the generated response can be sent to speech synthesis module 740 (e.g.,speech synthesizer) where it can be processed to synthesize the dialogueresponse in speech form. In yet other examples, the generated responsecan be data content relevant to satisfying a user request in the speechinput.

Speech synthesis module 740 can be configured to synthesize speechoutputs for presentation to the user. Speech synthesis module 740synthesizes speech outputs based on text provided by the digitalassistant. For example, the generated dialogue response can be in theform of a text string. Speech synthesis module 740 can convert the textstring to an audible speech output. Speech synthesis module 740 can useany appropriate speech synthesis technique in order to generate speechoutputs from text, including, but not limited to, concatenativesynthesis, unit selection synthesis, diphone synthesis, domain-specificsynthesis, formant synthesis, articulatory synthesis, hidden Markovmodel (HMM) based synthesis, and sinewave synthesis. In some examples,speech synthesis module 740 can be configured to synthesize individualwords based on phonemic strings corresponding to the words. For example,a phonemic string can be associated with a word in the generateddialogue response. The phonemic string can be stored in metadataassociated with the word. Speech synthesis model 740 can be configuredto directly process the phonemic string in the metadata to synthesizethe word in speech form.

In some examples, instead of (or in addition to) using speech synthesismodule 740, speech synthesis can be performed on a remote device (e.g.,the server system 108), and the synthesized speech can be sent to theuser device for output to the user. For example, this can occur in someimplementations where outputs for a digital assistant are generated at aserver system. And because server systems generally have more processingpower or resources than a user device, it can be possible to obtainhigher quality speech outputs than would be practical with client-sidesynthesis.

Additional details on digital assistants can be found in the U.S.Utility application Ser. No. 12/987,982, entitled “Intelligent AutomatedAssistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No.13/251,088, entitled “Generating and Processing Task Items ThatRepresent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosuresof which are incorporated herein by reference.

4. Exemplary Functions of a Digital Assistant

FIG. 8 illustrates a block diagram of functions of a digital assistant800 (or the server portion of a digital assistant). In some examples,digital assistant 800 is implemented using digital assistant module 726.Digital assistant 800 includes one or more modules, models,applications, vocabularies, and user data similar to those of digitalassistant module 726. For example, digital assistant 800 includes thefollowing sub-modules, or a subset or superset thereof: an input/outputprocessing module, an STT process module, a natural language processingmodule, a task flow processing module, and a speech synthesis module.These modules can also be implemented similar to that of thecorresponding modules as illustrated in FIG. 7B, and therefore are notshown and not repeatedly described.

In some examples, digital assistant 800 can further include an eventinformation detection module 820, an event agreement detection module840, an event type determination module 860, and an event descriptiongeneration module 880. These modules can be an integrated or separateportion of, for example, a natural language processing module. Forinstance, natural language processing module 732 integrates or leveragesevent information detection module 820.

As illustrated in FIG. 8, in some embodiments, digital assistant 800receives unstructured natural language information. Unstructured naturallanguage information can be in written form or in verbal form. In someexamples, unstructured natural language information includes a text/SMSmessage, an instant message, an electronic mail message, a speech inputsuch as a voice mail or a live conversation, or the like. For example,unstructured natural language information includes a plurality of textmessages or instant messages exchanged between or among multiple users.As another example, unstructured natural language information includesan electronic mail transcript or body containing multiple electronicmail messages exchanged between or among users. As another example,unstructured natural language information includes verbal communicationsbetween or among users.

In some examples, digital assistant 800 dynamically receives updatedunstructured natural language information. For example, digitalassistant 800 receives text messages or instant messages in real timewhile the users are exchanging the message. In some examples, theunstructured natural language information is updated dynamically toreflect the new messages the users provide. In some embodiments, if theunstructured natural language information is dynamically updated, auni-directional recurrent neural network (RNN) is used to process theunstructured natural language information, as described in more detailbelow.

As illustrated in FIG. 8, in response to receiving unstructured naturallanguage information, event information detection module 820 determineswhether even information is present within the unstructured naturallanguage information. As described above, unstructured natural languageinformation includes, for example, different types of information suchas text messages or electronic mails. In some examples, eventinformation detection module 820 may be implemented using differentnetworks corresponding to different types of information contained inthe unstructured natural language information. For example, if theunstructured natural language information includes information that isbeing dynamically updated (e.g., text messages that are being exchangedamong users, as shown in FIG. 9A), event information detection module820 can be implemented using a first network 920 shown in FIGS. 9B or9C. In some examples, the first network is a neural network, such as auni-directional recurrent neural network (RNN). As another example, ifthe unstructured natural language information includes the entireinformation being processed and is not dynamically updated (e.g., anelectronic mail transcript or body that represents an entireconversation among users, as shown in FIG. 10A), event informationdetection module 820 can be implemented using a second network 1020shown in FIGS. 10B or 10C. In some examples, the second network is aneural network, such as a bi-directional RNN. In some examples, eventinformation detection module 820 can be implemented using a singlenetwork for different types of unstructured natural languageinformation. For example, event information detection module 820 can beimplemented using the first network 920 (e.g., a uni-directional RNN)for unstructured natural language information that is or is notdynamically updated. In some examples, first network 920 and secondnetwork 1020 are implemented using one or more multifunction devicesincluding but not limited to devices 100, 200, 400, and 600 (FIGS. 1, 2,4, and 6B).

FIG. 9A illustrates an exemplary user interface 902 of an electronicdevice 900 according to various examples. With references to FIGS. 8 and9A, in some examples, digital assistant 800 receives multiple textmessages via user interface 902. The multiple text messages displayed onuser interface 902 represent a communication between a first user and asecond user. For example, the communication includes multiple messagesto arrange an event. In the example shown in FIG. 9A, the first userprovides a text message 904 such as “Lunch today at Caffe Macs?Noon-ish?” In response, the second user provides a text message 908 suchas “Won't work, I have a meeting until 12:30 . . . ” The first user thenprovides a text message 914 such as “Hmm, too crazy at 12:30. How about1:00?” The second user then provides a text message 918 such as “Soundsgood, see you then!” As shown in FIG. 9A, as the two users continuetheir conversation, digital assistant 800 receives dynamically updatedunstructured natural language information.

In some examples, unstructured natural language information (e.g., textmessages in FIG. 9A) is associated with one or more polarities. Apolarity refers to the classification of sentiment in unstructurednatural language information. For example, a polarity refers to theclassification of sentiment as it relates to event information. In someexamples, a polarity can be a proposal, a rejection, an acceptance, or ano-event. As shown in FIG. 9A, text messages 904 and 908 include one ormore words that represent a polarity of proposal (e.g., “today,”“Noon-ish,” “how about 1:00”). Text messages 908 and 914 include one ormore words that represent a polarity of rejection (e.g., Won't work,”“too crazy”). Text message 918 includes one or more words that representa polarity of acceptance (e.g., Sounds good,” “see you then”).Accordingly, unstructured natural language information, such as textmessages 904, 908, 914, 918, can include words representing multiplesproposal and/or multiple rejections. Words included in the unstructurednatural language information is used in the determination of whetherevent information is present and whether an agreement on an event ispresent, as described in more detail below.

In some examples, unstructured natural language information includesdate and time information. For example, as shown in FIG. 9A, textmessages 904, 908, and 914 include time information such as “noon-ish,”“12:30,” and “1:00.” In some examples, date and time information is usedin the determination of whether the determination of whether eventinformation is present, whether an agreement on an event is present, andthe event type, as described in more detail below. In some examples, theunstructured natural language information also include event typeinformation (e.g., “lunch,” “meeting”), which is used in thedetermination of the event type, as described in more detail below.

With references to FIGS. 9A-9C, in some examples, if the unstructurednatural language information includes information that is dynamicallyupdated (e.g., text messages that are being exchanged between twousers), event information detection module 820 is implemented using afirst network 920. In some examples, first network 920 is a RNNimplemented with long short-term memory (LSTM) hidden nodes. FIGS. 9Band 9C illustrate an exemplary first network 920 for event detection andclassification according to various examples. FIG. 9B is a compactrepresentation of first network 920 and FIG. 9C is an equivalentrepresentation of the first network with the time unfolded. For example,input units 954 (e.g., x(t)) of FIG. 9B are unfolded as input units954A-T (e.g., x(1), x(2), . . . x(T)) of FIG. 9C. Similarly,first-context units 942 (e.g., h(t)) of FIG. 9B are unfolded asfirst-context units 942A-T (e.g., h(1), h(2), . . . h(T)) of FIG. 9C.And output label units 932 (e.g., Z(t)) are unfolded as output labelunits 932A-T (e.g., Z(1), Z(2), . . . Z(T)). In the examples shown inFIGS. 9B and 9C, “t” represents time and is in discrete time steps suchas 1, 2, . . . T. FIGS. 9B and 9C are described below together.

With reference to FIGS. 8, 9B, and 9C, in some embodiments, digitalassistant 800 receives the unstructured natural language information,and event information detection module 820 determines one or morepolarities associated with the unstructured natural languageinformation. As described, in some examples, event information detectionmodule 820 is implemented using first network 920. First network 920 is,for instance, a neural network (e.g., a uni-directional RNN). Firstnetwork 920 includes multiple layers such as an input layer 950, one ormore hidden layers 940, and an output layer 930. In this example shownin FIGS. 9B and 9C, first network 920 includes a single hidden layer940. It will be appreciated, however, that in other examples, firstnetwork 920 can include one or more additional hidden layers to form adeeper network. Each layer of first network 920 includes a number ofunits. A layer can, for instance, include a single unit or includesmultiple units. These units, which in some examples are referred to asdimensions, neurons, or nodes (e.g., context nodes), operate as thecomputational elements of first network 920.

In some embodiments, to determine one or more polarities associated withthe unstructured natural language information, event informationdetection module 820 generates an input layer 950 based on theunstructured natural language information; generates a hidden layer 940based on the input layer 950; and generates an output layer 930 based onthe hidden layer 940. As illustrated in FIGS. 9B and 9C, in someexamples, input layer 950 includes a plurality of input units 954 (e.g.,x(t)) and preceding first-context units 952 (e.g., h(t−1)). Hidden layer940 includes a plurality of current first-context units 942 (e.g.,h(t)). Output layer 930 includes a plurality of output label units 932(e.g., z(t)) representing one or more polarities of at least a portionof the unstructured natural language information and a first-level eventtype output 934.

In some examples, input units 954 (e.g., x(t)) include a word sequenceobtained based on the unstructured natural language information. A wordsequence is a sequence or string of words. For example, as illustratedin FIG. 9A, the unstructured natural language information may include aplurality of words such as “lunch,” “today,” “Noon-ish,” “Won't,”“work,” “too,” “crazy,” etc. A word sequence is generated based on theplurality of words and indicates relative timing relation of the wordsincluded in the word sequence. For example, the word sequence indicatesthat the words “lunch,” “today,” and “Noon-ish” are received before thewords “Won't” and “work.” The timing relation of the words indicatestemporal information associated with a plurality of messages in theunstructured natural language information. For examples, the timingrelation of the words “lunch,” “today,” “Noon-ish,” “Won't” and “work”indicates that text message 904 (containing the words “lunch,” “today,”and “Noon-ish”) is received prior to text message 908 (containing thewords “Won't” and “work”). In some examples, temporal information isused to enhance the accuracy in determining whether event information ispresent and/or whether an agreement on an event is present. For example,by including the temporal information, first network 920 takes thetemporal dependencies of text messages 904 and 908 into consideration indetermining whether event information is present and/or whether anagreement on an event is present. In this example, text message 908(containing the words “Won't” and “work”) includes a rejection of theproposal included in text message 904 (containing the words “lunch,”“today,” and “Noon-ish”).

In some examples, input units 954 includes a plurality of tokensobtained based on the unstructured natural language information. Theplurality of tokens represent, for example, data and time informationand/or entities recognized based on a naming-entity vocabulary. Forexample, as illustrated in FIG. 9A, the unstructured natural languageinformation includes date and time information such as “today,” “12:30,”and “1:00.” In some examples, event information detection module 820generates a plurality of tokens representing these date and timeinformation. As another example, unstructured natural languageinformation includes references to entities such as “49ers,” “Giants,”“Warriors,” or the like. In some examples, event information detectionmodule 820 generates a plurality of tokens representing the entitiesbased on a named-entity recognition vocabulary. For example, using thevocabulary, one or more tokens (e.g., basketball team in San Francisco)can be generated based on the recognition that “Warriors” is abasketball team in San Francisco.

With references to FIGS. 9B and 9C, in some examples, each input unit954 represents a word or a token. As a result, the plurality of inputunits 954 represents one or more word sequences and/or one or moretokens. As illustrated in FIGS. 9B and 9C, in some examples, an inputunit 954 is represented as a vector having a dimension of N×1. As aresult, each input unit (e.g., a current input unit x(t)) may have a1-of-N encoding. In some examples, the N represents a total number ofwords and tokens within a vocabulary that is configured to determinewhether event information is present in the unstructured naturallanguage information. For example, the vocabulary includes a collectionof words or tokens that are known to be related to event information.

In some examples, input layer 950 includes or receives a plurality ofpreceding first-context units 952. A preceding first-context unit 952(e.g., h(t−1)) includes an internal representation of context from oneor more output values of a preceding time step in hidden layer 940. Asillustrated in FIGS. 9B and 9C, a preceding first-context unit 952(e.g., h(t−1)) is included in or received at input layer 950. In someexamples, a preceding first-context unit 952 and a current input unit954 (e.g., x(t)) are used in generating a current first-context unit 942(e.g., h(t)). For example, as shown in FIG. 9C, first-context unit 942B(e.g., h(2)) is generated based on an input unit 954B (e.g., x(2)) and apreceding first-context unit 942A (e.g., h(1)). As shown in FIG. 9B,preceding first-context units 952 are provided from hidden layer 940 toinput layer 950 via one or more first recurrent connections 943 (e.g.,recurrent connections 943A, 943B).

As described, hidden layer 940 includes a plurality of first-contextunits 942. In some examples, a first-context unit 942 (e.g., h(t)) isrepresented as a vector having a dimension of H×1. In a uni-directionalRNN such as first network 920, generating first-context units 942includes, for example, weighting a current input unit using a firstweight matrix, weighting a preceding first-context unit using a secondweight matrix, and determining a current first-context unit based on theweighting of the current input unit and the weighting of the precedingfirst-context unit. As illustrated in FIGS. 9B and 9C, in some examples,a current first-context unit 942 (e.g., h(t)) is generated according toformula (1) below.

h(t)=F{X·x(t)+W·h(t−1)}  (1)

In formula (1), x(t) represents a current input unit 954; h(t−1)represents a preceding first-context unit 952; h(t) represents a currentfirst-context unit 942; X represents a first weight matrix that has adimension of H×N; W represents a second weighting matrix that has adimension of H×H. In some embodiments, F{ } denotes an activationfunction, such as a sigmoid, a hyperbolic tangent, rectified linearunit, any function related thereto, or any combination thereof. Currentfirst-context unit 942 (e.g., h(t)) is indicative of a state of firstnetwork 920 and have a dimension of H×1.

As illustrated in FIGS. 9B and 9C, in some examples, first network 920includes an output layer 930. Output layer 930 includes a plurality ofoutput label units 932 (e.g., z(t)) and a first-level event type output934 (e.g., q). In some embodiments, output label units 932 andfirst-level event type output 934 are generated based on first-contextunits 942. For example, generating an output label unit 932 includesweighting a current first-context unit 942 using a fifth weight matrix,and determining a current output label unit 932 based on the weightingof the current first-context unit 942. As illustrated in FIGS. 9B and9C, in some examples, current output label unit 932 (e.g., z(t)) isgenerated according to formula (2) below.

z(t)=G{Z·h(t)}  (2)

In formula (2), h(t) represents a current first-context unit 942; Zrepresents a fifth weighting matrix; and G denotes a function such as asoftmax activation function. In some examples, each of the output labelunit has an 1-of-K encoding, with K representing a number of polaritiesof a pre-determined polarity set. For example, each output label unit932 (e.g., z(1), z(2), . . . z(T)) is associated with a polarity ofproposal, rejection, acceptance, or no-event. Correspondingly, in thisexample, K is equal to 4 and the output label unit 932 has a 1-of-4encoding. In some examples, each output label unit 932 corresponds to aninput unit 954, and each input unit 954 includes a word in a wordsequence or a token obtained from the unstructured natural languageinformation. Therefore, the polarities associated with output labelunits 932 represent one or more polarities associated with theunstructured natural language information. In some examples, as shown inFIGS. 9B and 9C, output layer 930 also include a first-level event typeoutput 934 (e.g., q), which represents a first-level event type.First-level event type output 934 is described in more detail below.

As described above, unstructured natural language information may be ormay not be dynamically updated. For example, the unstructured naturallanguage information includes multiple electronic mail messagesrepresenting the entire communication between two users and is notdynamically updated. FIG. 10A illustrates an exemplary user interface1000 displaying multiple electronic mail messages. With references toFIGS. 8, 10A, and 10B, in some examples, the multiple electronic mailmessages displayed on user interface 1000 represent an entirecommunication between a first user and a second user. For example, thecommunication may include multiple electronic mail messages between thetwo users to arrange an event. As shown in FIG. 10A, the body ofelectronic mail message 1010 includes an initial electronic mail sent bya first user (e.g., “Lunch today at Caffe Macs? Noon-ish?”), a secondelectronic mail sent by a second user in response to the initial emailof the first user (e.g., “Won't work, I have a meeting until 12:30 . . .”), a third electronic mail sent by the first user in response to thesecond email of the second user (e.g., “Hmm, too crazy at 12:30. Howabout 1:00?”), and a fourth electronic mail sent by the second user inresponse to the third email of the first user (e.g., “Sounds good, seeyou then!”). In this example, the body of electronic mail message 1010includes the entire communication between the first user and the seconduser for arranging an event (e.g., a lunch event), and is notdynamically updated.

In some examples, if the unstructured natural language information(e.g., the body of electronic mail message 1010) is not dynamicallyupdated, event information detection module 820 can be implemented usinga first network 920 as described above or using a second network 1020shown in FIGS. 10B or 10C. In some examples, second network 1020 is aneural network, such as a bi-directional RNN. In some examples, secondnetwork 1020 is a RNN implemented with long short-term memory (LSTM)hidden nodes). FIGS. 10B and 10C illustrate an exemplary second network1020 for event detection and classification according to variousexamples. FIG. 10B is a compact representation of second network 1020and FIG. 10C is an equivalent representation of the second network withthe time unfolded. For example, input units 1054 (e.g., x(t)) of FIG.10B are unfolded as input units 1054A-T (e.g., x(1), x(2), . . . x(T))of FIG. 10C. Similarly, first-context units 1042 (e.g., h(t)) of FIG.10B are unfolded as first-context units 1042A-T (e.g., h(1), h(2), . . .h(T)) of FIG. 10C. Second-context units 1044 (e.g., g(t)) of FIG. 10Bare unfolded as second-context units 1044A-T (e.g., g(1), g(2), . . .g(T)) of FIG. 10C. And output label units 1032 (e.g., Z(t)) of FIG. 10Bare unfolded as output label units 1032A-T (e.g., Z(1), Z(2), . . .Z(T)) of FIG. 10C. In the examples shown in FIGS. 10B and 10C, “t”represents time and is in discrete time steps such as 1, 2, . . . T.FIGS. 10B and 10C are described below together.

With reference to FIGS. 8, 10B, and 10C, in some embodiments, secondnetwork 1020 includes multiple layers such as an input layer 1050, oneor more hidden layers 1040, and an output layer 1030. In this example,second network 1020 includes a single hidden layer 1040. In someembodiments, to determine one or more polarities associated with theunstructured natural language information (e.g., the body of electronicmail message 1010 as shown in FIG. 10A), event information detectionmodule 820 generates input layer 1050 based on the unstructured naturallanguage information; generates hidden layer 1040 based on the inputlayer 1050; and generates an output layer 1030 based on the hidden layer1040.

Similar to input layer 950 of first network 920, input layer 1050 ofsecond network 1020 includes a plurality of input units 1054 (e.g.,x(t)). Each input unit 1054 (e.g., x(1), x(2)) represents a word ortoken obtained from the unstructured natural language information (e.g.,the body of electronic mail message 1010 as shown in FIG. 10A). An inputunit 1054 is represented as a vector having a dimension of N×1. As aresult, each input unit (e.g., a current input unit x(t)) may have a1-of-N encoding. In some examples, input layer 1050 also includes orreceives a plurality of preceding first-context units 1052 (e.g.,h(t−1)). Similar to preceding first-context unit 952 described above, apreceding first-context unit 1052 (e.g., h(t−1)) includes an internalrepresentation of context from one or more output values of a precedingtime step in the hidden layer 1040. As illustrated in FIGS. 10B and 10C,in some examples, a preceding first-context unit 1052 (e.g., h(t−1)) anda current input unit 1054 (e.g., x(t)) is used in generating a currentfirst-context unit 1042 (e.g., h(t)). Preceding first-context units 1052are provided from hidden layer 1040 to input layer 1050 via one or morefirst recurrent connections 1043 (e.g., recurrent connections 1043A,1043B). In some examples, a current first-context unit 1042 (e.g., h(t))is generated according to formula (1) (i.e., h(t)=F{Xx(t)+Wh(t−1)}) asdescribed above.

As illustrated in FIGS. 10B and 10C, in some examples, input layer 1050also includes or receives a plurality of following second-context units1056. A following second-context unit 1056 (e.g., g(t+1)) includes aninternal representation of context from one or more output values of afollowing time step (e.g., a future time step) in the hidden layer 1040.As illustrated in FIGS. 10B and 10C, in some examples, a followingsecond-context unit 1056 (e.g., g(t+1)) and a current input unit 1054(e.g., x(t)) are used in generating a current second-context unit 1044(e.g., g(t)). For example, as shown in FIG. 10C, a currentsecond-context unit 1044A (e.g., g(1)) is generated based on a currentinput unit 1054A (e.g., x(1)) and a following second-context unit 1044B(e.g., g(2)). As shown in FIGS. 10B and 10C, following second-contextunits 1056 are provided from hidden layer 1040 to input layer 1050 viaone or more second recurrent connections 1045 (e.g., recurrentconnections 1045A, 1045B).

As described, hidden layer 1040 includes a plurality of second-contextunits 1044. In some examples, a second-context unit 1044 (e.g., g(t)) isrepresented as a vector having a dimension of H×1. In a bi-directionalRNN such as second network 1020, generating second-context units 1044includes, for example, weighting a current input unit using a thirdweight matrix, weighting a following second-context unit using a fourthweight matrix, and determining a current second-context unit based onthe weighting of the current input unit and the weighting of thefollowing second-context unit. As illustrated in FIGS. 10B and 10C, insome examples, a current second-context unit 1044 (e.g., g(t)) isgenerated according to formula (3) below.

g(t)=F{Y·x(t)+V·g(t+1)}  (3)

In formula (3), x(t) represents a current input unit 1054; g(t+1)represents a following second-context unit 1056; g(t) represents thecurrent second-context unit 1044; Y represents a third weight matrixthat has a dimension of H×N; V represents a fourth weighting matrix thathas a dimension of H×H. In some embodiments, F{ } denotes an activationfunction, such as a sigmoid, a hyperbolic tangent, rectified linearunit, any function related thereto, or any combination thereof.

As illustrated in FIGS. 10B and 10C, in some examples, second network1020 includes an output layer 1030. Output layer 1030 includes aplurality of output label units 1032 (e.g., z(t)) and a first-levelevent type output 1034 (e.g., q). In some embodiments, output labelunits 1032 and first-level event type output 1034 are generated based onfirst-context units 1042 and second-context units 1044. For example,generating a current output label units 1032 includes obtaining acurrent state based on a current first-context unit and a currentsecond-context unit; weighting the current state using a sixth weightmatrix, and determining the current output label unit based on theweighting of the current state. The current state, for instance, isindicative of a state of second network 1020 and has a dimension of 2H.As illustrated in FIGS. 10B and 10C, in some examples, the current state(e.g., s(t)) is obtained by concatenation of a current first-contextunit (e.g., h(t)) and a current second-context unit (e.g., g(t)), asindicated in formula 4 below.

s(t)=[h(t)g(t)]  (4)

In formula (4), s(t) represents the current state of second network1020; h(t) represents current first-context unit 1042; and g(t)represents current second-context unit 1044.

In some embodiments, a current output label unit 1032 (e.g., z(t)) ofoutput layer 1030 is generated according to formula (5) below.

z(t)=G{Z·s(t)}  (5)

In formula (5), s(t) represent a current state of second network 1020; Zrepresents a sixth weighting matrix; and G denotes a function such as asoftmax activation function. In some examples, each of output labelunits 1032 has an 1-of-K encoding, with K representing a number ofpolarities of a pre-determined polarity set. For example, each outputlabel unit 1032 (e.g., z(1), z(2), . . . z(T)) is associated with apolarity of proposal, rejection, acceptance, or no-event.Correspondingly, in this example, K is equal to 4. In some examples,each output label unit 1032 corresponds to an input unit 1054, and eachinput unit 1054 includes a word in a word sequence or a token obtainedfrom the unstructured natural language information. Therefore, thepolarities associated with output label units 1032 represent thepolarities associated with the unstructured natural languageinformation. In FIGS. 10B and 10C, first-level event type output 1034(e.g., q) represents a first-level event type and is described in moredetail below.

With reference to FIGS. 8, 9A-9C, and 10A-10C, in some embodiments,after the determination of one or more polarities associated with theunstructured natural language information, event information detectionmodule 820 determines whether event information is present based on theone or more polarities. In some embodiments, event information detectionmodule 820 determines whether the one or more polarities associated withoutput label units 932 or 1032 include at least one of a proposal, arejection, or an acceptance. In accordance with a determination that theone or more polarities include at least one of a proposal, a rejection,or an acceptance, event information detection module 820 determines aprobability associated with the at least one of a proposal, a rejection,or an acceptance; and determines whether the probability satisfies afirst probability threshold. In accordance with a determination that theprobability satisfies the first probability threshold, event informationdetection module 820 determines that event information is present in theunstructured natural language information.

For example, as shown in FIGS. 8, 9A, and 10A, unstructured naturallanguage information includes a plurality of portions. A portion of theunstructured natural language information includes one or more wordsand/or tokens, and can be word(s), sentence(s), paragraph(s), ormessage(s). A portion can represent, for example, a text message, anelectronic mail message, or the like. As described above, in someembodiments, event information detection module 820 determines, for eachof the words and/or tokens, a polarity (e.g., proposal, rejection,acceptance, or no-event) associated with it. In some examples, eventinformation detection module 820 further determines, for each portion ofthe unstructured natural language information, a probability that theportion of the unstructured natural language information is associatedwith a particular polarity. For example, as shown in FIG. 9A, textmessage 908 includes words and tokens such as “Won't work, I have ameeting until 12:30.” Each of these words and tokens is associated witha polarity. For example, each of the words “Won't work” is associatedwith a rejection polarity; and each of the words and tokens “I have ameeting until 12:30” is associated with a rejection or proposalpolarity. Based on the polarities of the words and tokens in textmessage 908, event information detection module 820 determines theprobability that text message 908 is associated with a particularpolarity. For example, event information detection module 820 determinesthat the probability that text message 908 is associated with arejection polarity is about 90%; the probability that text message 908is associated with a proposal polarity is about 20%; and the probabilitythat text message 908 is associated with an acceptance polarity or ano-event polarity is about 0%. In some examples, event informationdetection module 820 compares the probabilities with a first probabilitythreshold (e.g., 50%) and determines that the probability that textmessage 908 is a rejection satisfies the first probability threshold.Accordingly, event information detection module 820 determines thatevent information is present in the unstructured natural languageinformation. In some examples, the probability associated with at leasta portion of the unstructured natural language is a probabilitydistribution.

In some embodiments, the unstructured natural language informationincludes an entire communication between two users (e.g., multiple textmessages as shown in FIGS. 9A and 10A. It is appreciated that theunstructured natural language information can also includecommunications between multiple users across any period of time. Forexample, the communications can include messages that the usersexchanged in the past few minutes, hours, days, weeks, months, or years.It is also appreciated that a uni-directional RNN (e.g., first network920) or a bi-directional RNN (e.g., second network 1020) can be used todetermine whether event information is present based on any unstructurednatural language information. For example, the RNNs can be used forunstructured natural language information having a time period of anumber of minutes, hours, days, weeks, months, or years.

With reference back to FIG. 8, in some embodiments, in accordance with adetermination that event information is present within the unstructurednatural language information, an event agreement detection module 840determines whether an agreement on an event is present in theunstructured natural language information. In some examples, eventagreement detection module 840 determines whether the one or morepolarities associated with at least a portion of the unstructurednatural language information include an acceptance. In accordance with adetermination that the one or more polarities include an acceptance,event agreement detection module 840 determines a probability associatedwith the acceptance; and determines whether the probability satisfies asecond probability threshold. In accordance with a determination thatprobability satisfies the second probability threshold, event agreementdetection module 840 determines that an agreement on an event ispresent.

As described, in some examples, event information detection module 820determines one or more probabilities associated with the at least aportion of the unstructured natural language information (e.g., a word,a sentence, a paragraph, a message). For example, as shown in FIG. 10A,the body of electronic mail message 1010 includes a sentence such as“Sounds good, see you then.” Each of the words in the sentence isassociated with a polarity. As described, the polarity associated with aword is determined by taking context (e.g., preceding words and/orfollowing words) of the word into account. In some examples, eventagreement detection module 840 determines whether at least a portion(e.g. a word, a sentence, a paragraph, or an electronic mail message) ofthe unstructured natural language information is associated with anacceptance polarity. If at least a portion is associated with anacceptance polarity, event agreement detection module 840 determines theprobability associated with an acceptance and determines whether anagreement is present. For example, event agreement detection module 840determines that the probability that the sentence of “Sounds good, seeyou then” is associated with an acceptance polarity is 90%. Eventagreement detection module 840 compares the probability of 90% with asecond probability threshold (e.g., 50%), and determines that theprobability associated with an acceptance satisfies the secondprobability threshold. Accordingly, the event agreement detection module840 determines that an event agreement is present in the unstructurednatural language information. In some examples, the probabilityassociated with at least a portion of the unstructured natural languageis a probability distribution.

In some embodiments, the first probability threshold and/or the secondprobability threshold are configurable based on user-specific data,historical data, or other data or criteria. In some embodiments, atleast one probability threshold is user-adjustable based on a user'spreferences. For example, a user may configure the acceptance thresholdhigher than a default value, if that user does not wish to receive eventdescriptions (described below) unless there is a very high degree ofcertainty that event information is present and/or an agreement ispresent in the unstructured natural language information. Users whowould prefer to receive more event descriptions, and who are notconcerned about false positives, may configure the threshold lower.

With reference back to FIG. 8, in some embodiments, in accordance with adetermination that an agreement on an event is present, an event typedetermination module 860 determines an event type of the event. An eventtype represents a category or sub-category of the event. To accuratelyprovide event descriptions, an accurate determination of the event typeis desired and important. In some embodiments, the determination of theevent type is based on a hierarchical classification using a naturallanguage event ontology.

FIGS. 11A-11H illustrate an exemplary natural language event ontology1100. In some examples, natural language event ontology 1100 is ahierarchical structure including a plurality of levels. For instance, asillustrated in FIGS. 11A-11B, natural language event ontology 1100includes a first level 1120 and a second level 1140. Each level includesa plurality of event type nodes. The event type nodes of second level1140 are child nodes of the first level 1120. A child nodehierarchically relates to a parent node. For example, as illustrated inFIGS. 11A-11B, natural language event ontology 1100 includes a root node1110, which a parent node to a plurality of first-level event type nodessuch as nodes 1122, 1124, 1126, and 1128. The first-level event typenodes 1122, 1124, 1126, and 1128 are child nodes of root node 1110. Insome embodiments, the first-level event type nodes 1122, 1124, 1126, and1128 represent a coarse classification of event types. For example, atfirst level 1120, events may be coarsely classified as gathering,entertainment, appointment, and arrangement. Correspondingly, firstlevel 1120 includes a gathering event type node 1122, an entertainmentevent type node 1124, an appointment event type node 1126, and anarrangement event type node 1128.

As described, natural language event ontology 1100 includes a secondlevel 1140. In some embodiments, at second level 1140, each event typenode is a child node of a corresponding first-level event type node. Thesecond-level event type nodes represent a finer classification of eventtypes than the first-level event type nodes. For example, a gatheringevent type may be further classified to include, for example,second-level event types such as meal, party, drinks, ceremony, reunion,field trip, anniversary, or the like. Correspondingly, gathering eventtype node 1122 of first level 1120 is a parent node of a meal event typenode 1142A, a party event type node 1142B, a drinks event type node1142C, a ceremony event type node 1142D, a reunion event type node1142E, a field trip event type node 1142F, and an anniversary event typenode 1142G. As shown in FIGS. 11A-11B, the hierarchical relation betweena first-level event type node (e.g., gathering event type node 1122) andthe plurality of second-level event type nodes (e.g., second-level eventtype node 1142A-G) are represented by directional connections 1132(solid lines). The direction of a connection 1132 indicates aparent-child relation of the event type nodes. For example, thedirection of a connection 1132 connecting meal event type node 1142A andgathering event type node 1122 indicates that meal event type node 1142Ais a child node that belongs to gathering event type node 1122.

As shown in FIGS. 11A-11B, similarly, an entertainment event type may befurther classified to include, for example, second-level event typessuch as movies, culture, park, sport, game, hike, karaoke, or the like.Correspondingly, entertainment event type node 1124 at first level 1120is a parent node to a plurality of second-level event type nodesrepresenting these second-level event types. An appointment event typemay be further classified to include, for example, meeting, health,wellness, beauty, hairdresser, education, interview, or the like.Correspondingly, appointment event type node 1126 at first level 1120 isa parent node to a plurality of second-level event type nodesrepresenting these second-level event types. An arrangement event typemay be further classified to include, for example, shopping, pickup,travel, or the like. Correspondingly, arrangement event type node 1128at first level 1120 is a parent node to a plurality of second-levelevent type nodes representing these second-level event types. It isappreciated that natural language event ontology 1100 can include anydesired number of levels and each level can have any desired number ofevent type nodes.

In some examples, at least one event type node is associated with apriority rule. A priority rule indicates a relative priority of oneevent type node with respect to another event type node. For example, asshown in FIGS. 11A-11B, at second level 1140, meal event type node1142A, party event type node 1142B, drinks event type node 1142C, andfield trip event type node 1142F are associated with one or morepriority rules. The priority rules are represented by directionalconnections 1143A-D (broken lines). The direction of a connection 1143indicates a relative priority level between two associated event typenodes. For example, the direction of connection 1143A is from meal eventtype node 1142A to party event type node 1142B, indicating that mealevent type node 1142A has a lower priority than party event type node1142B. Similarly, drinks event type node 1142C has a lower priority thanmeal event type node 1142A. As describe in more detail below, in someexamples, unstructured natural language information includes eventinformation that is associated with more than one event type nodes innatural language event ontology 1100. A priority rule may be used todetermine the event type of the event based on the plurality of eventtype nodes associated with the priority rule. For example, if theunstructured natural language information includes event information(e.g., “pizza and drinks for dinner?”) that is associated with both mealevent type node 1142A and drinks event type node 1142C, the priorityrule associated with these two nodes can be used to determine the eventtype of the event. According to the priority rule as shown in FIG. 11A,in some examples, event type determination module 860 determines thatthe event type is meal because meal event type node 1142A has a higherpriority than drinks event type node 1142C.

In some embodiments, at least one event type node of natural languageevent ontology 1100 is a leaf node. A leaf node does not hierarchicallyrelate to a child node. For example, as illustrated in FIGS. 11A-11B,hairdresser event type node 1146A and travel event type node 1146B areleaf nodes, indicating that they are not parents of a child node.

As illustrated in FIGS. 11C-11G, in some embodiments, natural languageevent ontology 1100 includes one or more higher levels 1160 thathierarchically relates to second level 1140. The event type nodes of theone or more higher levels 1160 are child nodes of the correspondingevent type node of second level 1140. For example, as shown in FIG. 11C,a meal event type may be further classified to include, for example,third-level event types such as breakfast, brunch, lunch, snack,ice-cream, potluck, BBQ, picnic, dinner, or the like. Correspondingly,meal event type node 1142A at second level 1140 is a parent node to aplurality of third-level event type nodes representing these third-levelevent types. In some examples, one or more of the third-level eventtypes may be further classified to include higher level event types. Asan example, a brunch event type may be classified to include a birthdaybrunch. As another example, a lunch event type may be classified toinclude an Easter lunch, a Christmas lunch, a Thanksgiving lunch, a teamlunch, a family lunch, a birthday lunch, or the like. As anotherexample, a dinner event type may be classified to include a Easterdinner, a Christmas dinner, a Thanksgiving dinner, a team dinner, afamily dinner, a birthday dinner, a movie dinner, or the like.Correspondingly, the third-level event type nodes (e.g., breakfast eventtype node, brunch event type node, etc.) are parent nodes to respectivehigher-level event type nodes representing these higher-level eventtypes. In some examples, the higher-level event type nodes include oneor more leaf nodes (e.g., Easter lunch, Christmas lunch, or the like).As shown in FIG. 11C, in third- and higher-level event type nodes, atleast one event type node is associated with a priority rule. Forexample, the lunch event type node and the dinner event type node bothhave lower priority than the picnic event type node.

As shown in FIG. 11D, in the second level 1140, an anniversary eventtype may be further classified to include third-level event types suchas birthday or the like. A party event type may be further classified toinclude third-level event types such as cocktail party, birthday party,graduation, retirement, house warming, Halloween, new year's party,Chinese new year, Cinco de mayo, Christmas holiday party, Masqueradeball, baby shower, bridal shower, St Patrick's day, Diwali, or the like.A drinks event type may be further classified to include third-levelevent types such as happy hour, coffee, tea, hot chocolate, Oktoberfest,or the like. A ceremony event type may be further classified to includethird level event types such as morning ceremony, communion,confirmation, Baptism, Bar\Bat Mitzvah, wedding, or the like.Correspondingly, the second-level event type nodes (e.g., anniversaryevent type node, party event type node, drinks event type node, andceremony event type node) are parent nodes of respective third levelevent type nodes as illustrated in FIG. 11D. In some examples, one ormore of the third-level event types may be further classified to includehigher-level event types. In some embodiments, some of the third- andhigher-level event type nodes are leaf nodes.

With reference to FIGS. 11E and 11F, an entertainment event type may befurther classified to include, for example, second-level event typessuch as movies, culture, park, sport, games, karaoke, hike, or the like.Correspondingly, entertainment event type node 1124 at first level 1120is the parent node of second-level event type nodes representing thesesecond-level event types. In second level 1140, a movie event type maybe further classified to include third-level event types such cinema anddinner. A culture event type may be further classified to includethird-level event types such as exhibition, theater, concert, opera,show, festival, or the like. A park event type may be further classifiedto include third-level event types such as amusement park, water park,or the like. A sport event type may be further classified to includethird level event types such as football, tennis, running, marathon,soccer, cricket, hockey, baseball, basketball, golf, skateboarding,snowboarding, rugby, yoga, volleyball, table tennis, basketball, bike,softball, skiing, or the like. A games event type may be furtherclassified to include third level event types such as poker, boardgames, bowling, game night, video games, or the like. A karaoke eventtype may be further classified to include third level event types suchas karaoke night. Correspondingly, the second-level event type nodes(e.g., movies event type-node, culture event type node, park event typenode, sport event type node, games event type node, and karaoke eventtype node) are parent nodes of respective third-level event type nodesas illustrated in FIGS. 11E and 11F. In some examples, one or more ofthe third-level event types may be further classified to includehigher-level event types, as shown in FIGS. 11E and 11F. In someembodiments, some of the third- and higher-level event type nodes areleaf nodes.

With reference to FIGS. 11G, an appointment event type may be furtherclassified to include, for example, child event types such as meeting,health, wellness, beauty, education, or the like. Correspondingly, theappointment event type node 1126 at first level 1120 is the parent nodeof second-level event type nodes representing these second-level eventtypes. In second level 1140, a meeting event type may be furtherclassified to include third-level event types such 1:1 meeting, businessmeeting, or the like. A health event type may be further classified toinclude third-level event types such as dentist, dermatologist,optometry, chiropractor, or the like. A wellness event type may befurther classified to include third-level event types such as SPA,massage, reflexology, nutritionist, acupuncture, or the like. A beautyevent type may be further classified to include third level event typessuch as manicure, pedicure, or the like. An education event type may befurther classified to include third level event types such as seminar,conference, workshop, training, class, keynote, congress, or the like.Correspondingly, the second-level event type nodes (e.g., interview,meeting, health, wellness, beauty, education) are parent nodes ofrespective third-level event type nodes as illustrated in FIG. 11G. Insome examples, one or more of the third-level event types may be furtherclassified to include higher-level event types. In some embodiments,some of the third- and higher-level event type nodes are leaf nodes.

With reference to FIG. 11H, an arrangement event type may be furtherclassified to include, for example, second-level event types such asshopping, travel, pickup, or the like. Correspondingly, the arrangementevent type node 1128 at first level 1120 is the parent node ofsecond-level event type nodes representing these second-level eventtypes. In second level 1140, a shopping event type may be furtherclassified to include third-level event types such as sales or the like.A pickup event type may be further classified to include third-levelevent types such as conference or the like. Correspondingly, thesecond-level event type nodes (e.g., shopping, travel, and pickup) areparent nodes of respective third-level event type nodes as illustratedin FIG. 11H. In some examples, one or more of the third-level eventtypes may be further classified to include higher-level event types. Insome embodiments, some of the third- and higher-level event type nodesare leaf nodes.

With reference to FIG. 12A, in some embodiments, event typedetermination module 860 of a digital assistant determines the eventtype using natural language event ontology 1100. In some examples, eventtype determination module 860 includes a first-level event typeclassifier 1202, a second- and higher-levels event type classifier 1204,and an event type adjuster 1206. In operation, for instance, first-levelevent type classifier 1202 determines a first-level event typeassociated with the event based on the unstructured natural languageinformation, and second and higher levels event type classifier 1204determines the event type based on the first-level event type.

As illustrated in FIGS. 11A-11B, in some examples, first level 1120 ofnatural language event ontology 1100 includes a plurality of first-levelevent type nodes such as gathering event type node 1122, entertainmentevent type node 1124, appointment event type node 1126, and arrangementevent type node 1128. The first-level event type nodes represent acoarse classification of the event types. Accordingly, in some examples,determining the first-level event type includes determining whether theevent information included in the unstructured natural languageinformation corresponds to one of the plurality of first-level eventtype nodes included in natural language event ontology 1100.

With references to FIGS. 9B, 9C, and 12A, first-level event typeclassifier 1202 determines the first-level event type based on afirst-level event type output of a network. As described, in someembodiments, a uni-directional RNN such as first network 920 is used todetermine a first-level event type output 934 (e.g., q), whichrepresents a first-level event type. In some examples, first-level eventtype classifier 1202 is a portion of event information detection module820 that implements first network 920. In some examples, first-levelevent type classifier 1202 is a portion that is separate from eventinformation detection module 820 and implements at least part of firstnetwork 920.

In some embodiments, to determine first-level event type output 934,first-level event type classifier 1202 weights a trailing first-contextunit of a hidden layer, and determines the first-level event type output934 based on the weighting of the trailing first-context unit. Atrailing first-context unit is the ending first-context unit in thesequence of first-context units of a hidden layer. For example, thetrailing first-context unit is the first-context unit at time t=T, whereT is the last time step in a sequence h(t). As illustrated in FIGS. 9Band 9C, first-context unit 932T (e.g., h(T)) of hidden layer 940 is thetrailing first-context unit. In some examples, first-level event typeoutput 934 (e.g., q) is generated according to formula (6) below,

q=G{Q·g(T)})   (6)

In formula (6), h(T) represents the trailing first-context unit, Qrepresents a seventh weighting matrix, and G denotes a function such asa softmax activation function. In some embodiments, first-level eventtype output 934 has an 1-of-L encoding. The L represents a number (e.g.,4) of the first-level event type nodes of natural language eventontology 1100.

With references to FIGS. 10B, 10C, and 12A, first-level event typeclassifier 1202 determines the first-level event type based on afirst-level event type output of a network. As described, in someembodiments, a bi-directional RNN such as second network 1020 is used todetermine a first-level event type output 1034 (e.g., q), whichrepresents a first-level event type. In some examples, first-level eventtype classifier 1202 is a portion of event information detection module820 that implements second network 1020. In some examples, first-levelevent type classifier 1202 is a portion that is separate from eventinformation detection module 820 and implements at least part of secondnetwork 1020.

In some embodiments, to determine first-level event type output 1034,first-level event type classifier 1202 concatenates a trailingfirst-context unit and a leading second-context unit, weights theconcatenation of the trailing first-context unit and the leadingsecond-context unit, and determines the first-level event type output1034 based on the weighting of the concatenation. As described, atrailing first-context unit is the ending unit in the sequence offirst-context units of a hidden layer. As illustrated in FIGS. 10B and10C, first-context unit 1042T (e.g., h(T)) of hidden layer 1040 is thetrailing first-context unit. A leading second-context unit is thebeginning second-context unit in the sequence of second-context units ofthe hidden layer. For example, the leading second-context unit is thesecond-context unit at time t=1, which represents the first time step inthe sequence g(t). As illustrated in FIGS. 10B and 10C, second-contextunit 1044A (e.g., g(1)) of hidden layer 1040 is the leadingsecond-context unit. As illustrated in FIGS. 10B and 10C, in someexamples, first-level event type output 1034 (e.g., q) is generatedaccording to formula (7) below,

q=G{Q·[h(T) g(1)])}  (7)

In formula (7), h(T) represents the trailing first-context unit, g(1)represents the leading second-context unit, [h(T) g(l)] denotes aconcatenation function, Q represents an eighth weighting matrix, and Gdenotes a function such as a softmax activation function. In someembodiments, first-level event type output 1034 has an 1-of-L encoding.The L represents a number (e.g., 4) of the first-level event type nodesof natural language event ontology 1100.

In some examples, first-level event type classifier 1202 determines thefirst-level event type using first-level event type output 934 or 1034.As illustrated in FIG. 12B, first-level event type classifier 1202receives unstructured natural language information including, forexample, “Hey, pizza for dinner?” First-level event type classifier 1202determines first-level event type outputs 934 or 1034 based on theunstructured natural language information. Based on first-level eventtype outputs 934 or 1034, first-level event type classifier 1202determines, that the first-level event type is, for example, gathering.

With references to FIGS. 12A and 12B, in some embodiments, second andhigher levels event type classifier 1204 determines the event typeassociated with the unstructured natural language information based onthe first-level event type provided by first-level event type classifier1202. In some examples, second and higher levels event type classifier1204 determines, for each second-level event type node, a number ofcorrelations between the second-level event type node and theunstructured natural language information. Based on the number ofcorrelations of each second-level event type node, second and higherlevels event type classifier 1204 determines a second-level event type.

As illustrated in FIG. 12A, first-level event type classifier 1202provides the first-level event type (e.g., gathering) to second andhigher levels event type classifier 1204. For each second-level eventtype node that is a child node of a first-level event type noderepresenting the first-level event type (e.g., gathering), second andhigher levels event type classifier 1204 determines a number ofcorrelations between the second-level event type node and theunstructured natural language information. For example, as illustratedin FIG. 11A, first-level event type node 1122 is a parent node of aplurality of second-level event type nodes such as meal event type node1142A, party event type node 1142B, and drinks event type node 1142C. Asshown in FIGS. 11A and 12B, second and higher levels event typeclassifier 1204 determines the number of correlations between meal eventtype node 1142A and the unstructured natural language information (e.g.,“Hey, pizza for dinner?”). For example, using regular expressionpatterns, second and higher levels event type classifier 1204 determinesthat both the words “pizza” and “dinner” in the unstructured naturallanguage information correlate to meal event type node 1142A. As aresult, second and higher levels event type classifier 1204 determinesthat the number of correlation for meal event type node 1142A is two,indicated by second-level event type output 1242 in FIG. 12B. Similarly,second and higher levels event type classifier 1204 determines thatthere is no correlation between the unstructured natural languageinformation and party event type node 1142B or drinks event type node1142C. As a result, second and higher levels event type classifier 1204determines that the number of correlation for both party event type node1142B and drinks event type node 1142C is zero, indicated bysecond-level event type outputs 1244 and 1246 in FIG. 12B.

In some embodiments, to determine the second-level event type, secondand higher levels event type classifier 1204 determines the maximumnumber of correlations based on the number of correlations of eachsecond-level event type node and determines the second-level event typebased on the maximum number of correlations. For instance, as shown inFIGS. 11A and 12B, based on second-level event type outputs 1242, 1244,and 1246, which indicate the number of correlations of each second-levelevent type node 1142A-C, the second and higher levels event typeclassifier 1204 determines that the maximum number of correlations istwo, which is the number of correlations between the meal type eventnode 1142A and the unstructured natural language information (e.g.,“Hey, pizza for dinner?). Accordingly, the second-level event type isdetermined to be the meal, indicated by second-level event type output1242 in FIG. 12B.

In some embodiments, second and higher levels event type classifier 1204hierarchically determines a higher-level event type based on thesecond-level event type. For example, as illustrated in FIG. 12B,similar to those described above with respect to determining thesecond-level event type, second and higher levels event type classifier1204 determines a third-level event type to be dinner, indicated bythird-level event type output 1266, because third-level event typeoutput 1266 corresponds to the maximum correlations between thethird-level event type nodes (e.g., brunch event type node, lunch eventtype node, and dinner event type node) and the unstructured naturallanguage information (e.g., “Hey, pizza for dinner?).

In some examples, second and higher levels event type classifier 1204hierarchically determines a higher-level event type until a leaf node isreached and/or until the number of correlations of each higher-levelevent type node is zero. For example, as illustrated in FIG. 12B,fourth-level event type outputs 1282, 1284, and 1286 correspond to leafnodes in natural language event ontology 1100. As result, second andhigher levels event type classifier 1204 stops the determination of ahigher-level event type beyond the fourth-level. As another examples,there is no correlation between the unstructured natural languageinformation and fourth-level event type nodes (e.g., team dinner eventtype node, birthday dinner event type node, and family dinner event typenode), and therefore second and higher levels event type classifier 1204stops determining a higher-level event type beyond the fourth-level.

In some embodiments, second and higher levels event type classifier 1204determines the event type associated with the unstructured naturallanguage information based on the second-level event type or a higherlevel event type. As illustrated in FIG. 12B, the second-level eventtype is dinner and there is no higher-level event type. As a result,second and higher levels event type classifier 1204 determines that theevent type associated with the unstructured natural language informationis dinner.

With reference to FIG. 12C, in some embodiments, second and higherlevels event type classifier 1204 further determines whether at leasttwo second-level event type nodes correspond to the maximum number ofcorrelations. In accordance with the determination that at least twosecond-level event type nodes correspond to the maximum number ofcorrelations, second and higher levels event type classifier 1204determines the second-level event type based on one or more priorityrules. For instance, as illustrated in FIG. 12C, first-level event typeclassifier 1202 receives unstructured natural language information suchas “Hey, pizza and drinks at 7pm?” First-level event type classifier1202 determines that the first-level event type is, for example,gathering, indicated by first-level event type output 1222. Second andhigher levels event type classifier 1204 determines that, at the secondlevel, the number of correlations for both meal type event node anddrinks type event node is 1, and the number of correlations for partytype event node is 0. As a result, second and higher levels event typeclassifier 1204 determines that the maximum number of correlations is 1and determines that at least two second-level event type nodescorrespond to the maximum number of correlations.

As illustrated in FIG. 12C, in accordance with the determination that atleast two second-level event type nodes correspond to the maximum numberof correlations, second and higher levels event type classifier 1204determines the second-level event type based on one or more priorityrules. As shown in FIG. 11A and 12C, in some examples, meal event typenode 1142A and drinks event type node 1142C are associated with apriority rule represented by connection 1143B. And the direction ofconnection 1143B is from drinks event type node 1142C to meal event typenode 1142A, indicating that meal event type node 1142A has a higherpriority than drinks event type node 1142C. Correspondingly, second andhigher levels event type classifier 1204 determines that the meal eventtype output 1242 has a high priority than drinks event type output 1244.And therefore the second-level event type is meal, indicated bysecond-level event type output 1242.

As illustrated in FIGS. 12A and 12C, in some embodiments, event typeadjuster 1206 adjusts the determination of at least one of thefirst-level event type, the second-level event type, and a higher-levelevent type based on one or more tokens of the unstructured naturallanguage information. As described, one or more tokens can be generatedbased on the unstructured natural language information such as “Hey,pizza and drinks at 7 pm?” In some examples, a token is generated torepresent the time information of “7 pm.” Based on such token, eventtype adjuster 1206 adjusts the determination of third-level event type.For example, similar to those described above, as shown in FIG. 12C,first-level event type classifier 1202 determines that the first-levelevent type is gathering. Second and higher level event type classifier1204 determines that the second-level event type is meal. Instead ofhierarchically determining a third-level event type, event type adjuster1206 adjusts the third-level event type determination using the tokenrepresenting the time information of “7 pm.” For example, event typeadjuster 1206 determines that a meal starts at 7pm is probably a dinner.As a result, event type adjuster 1206 determines that the third-levelevent type is dinner, as indicated by third-level event type output 1266in FIG. 12C.

With reference back to FIG. 8, in some embodiments, event descriptiongeneration module 880 provides event description based on the event typedetermined by event type determination module 860. For example, eventdescription generation module 880 provides an event title, a startingtime of the event, an ending time of the event, a duration of the event,a location of the event, participants of the event, or the like. Theevent title includes a subject of a corresponding event type node. Forexample, the event title may include “dinner,” “coffee,” “lunch,” or thelike.

In some embodiments, event description generation module 880 providesthe event description based on context information. In some examples,context information includes user-specific data such as location data,date and time information, user's preferences, user's historical data,and/or event-specific data. As an example, user's GPS location dataindicates that the user is located in U.S. In average, the staring timefor a drinks event in U.S. starts at 6 p.m. and the duration of a drinksevent is about 2 hours. As a result, in this example, the eventdescription indicates the title of the event is drinks; the stating timeof the event is 6 p.m.; and the duration of the event is 2 hours. Asanother example, event type determination module 860 determines that theevent type is brunch and context information indicates the event date ismother's day. Accordingly, event description generation module 880provides the event title as “Mother's day brunch.” As another example,event type determination module 860 determines that the event type isdinner. And context information indicates the user's location is inSpain and indicates that dinner in Spain usually starts at 7 p.m.Accordingly, event description generation module 880 provides thestarting time of the dinner to be 7 p.m.

With reference to FIGS. 8 and 13, in some embodiments, digital assistant800 instantiates a process (e.g., a calendar process) using the eventdescription. Instantiating a process includes invoking the process ifthe process is not already running. If at least one instance of theprocess is running, instantiating a process includes executing anexisting instance of the process or generating a new instance of theprocess. For example, as shown in FIG. 13, digital assistant 800 invokesa calendar process 1302 to generate a calendar entry 1304 based on theevent description and display the calendar entry 1304. For instance,calendar entry 1304 provides the title 1308 of the event (e.g., Drinks),the date 1309 of the event (e.g., Friday, Mar. 11), the starting time1310 of the event (e.g., 6 p.m.), and the ending time 1311 of the event(e.g., 8 p.m.).

In some embodiments, calendar entry 1304 further provides one or moreaffordances 1312 and 1314. The one or more affordances enable receivingone or more user inputs with respect to the calendar entry 1304. Forexample, the user may select affordance 1312 to add calendar entry 1304to the user's calendar. The calendar process receives the user'sselection of affordance 1312 to confirm and add the calendar entry. Inresponse to receiving the user's selection of affordance 1312, thecalendar process 1302 adds the calendar entry to the user's calendar.

In some embodiments, the user may desire to edit calendar entry 1304.For example, calendar entry 1304 is editable with respect to thecalendar items such as title 1308, date 1309, starting time 1310, endingtime 1311, or the like. The user can use an input device (e.g., a mouse,a joystick, a finger, a keyboard, a stylus, or the like) to edit thecalendar items. After receiving one or more user inputs editing thecalendar entry 1304, the calendar process 1302 adds the edited calendarentry to the user's calendar.

In some embodiments, the user may desire to deny the calendar entry. Forexample, the user may select affordance 1314 to cancel the calendarentry 1304. In response to receiving the user's selection of affordance1314, the calendar process 1302 cancels the calendar entry 1304. In someexamples, the digital assistant initiates a dialog with the user tofurther clarify the event description.

In some embodiments, calendar process 1302 generates calendar entry 1304based on the event description and automatically add the calendar entry1304 to the user's calendar. For example, digital assistant 800determines a confidence level associated with the event informationdetection, the event agreement detection, the event type determination,the event description generation, or a combination thereof. Digitalassistant 800 further determines that the confidence level satisfies athreshold and therefore automatically adds the calendar entry 1304 tothe user's calendar.

In some embodiments, digital assistant 800 further generates a bookingrequest based on the event description. For example, the eventdescription indicates the event is a wedding in a location that isdifferent from the user's current location. Based on the eventdescription, digital assistant 800 generates a request to book flight tothe wedding location at the date and time indicated in the eventdescription. It is appreciated that the event description can beprovided to any desired applications, such as message applications,electronic mail applications, social media applications, or the like.

5. Process for Providing an Event Description

FIGS. 14A-J illustrate a flow diagram of an exemplary process 1400 foroperating a digital assistant in accordance with some embodiments.Process 1400 may be performed using one or more devices 104, 108, 200,400, or 600 (FIGS. 1, 2A, 4, or 6A-B). Operations in process 1400 are,optionally, combined or split and/or the order of some operations is,optionally, changed.

With reference to FIG. 14A, at block 1402, unstructured natural languageinformation is received from at least one user. At block 1403, theunstructured natural language information includes a message. At block1404, the at least one message includes a text message. At block 1405,the at least one message includes a speech input. At block 1406, theunstructured natural language information includes a plurality ofmessages. At block 1407, the unstructured natural language informationcomprises at least one electronic mail message.

At block 1410, in response to receiving the unstructured naturallanguage information, it is determined whether event information ispresent in the unstructured natural language information, for example,as described above with reference to FIGS. 8, 9A-9C, and 10A-10C. Atblock 1411, it is determined one or more polarities associated with theunstructured natural language information. At block 1412, an input layeris generated based on the unstructured natural language information. Theinput layer comprises a plurality of input units and a plurality ofpreceding first-context units. At block 1413, a word sequence isobtained based on the unstructured natural language information. Atblock 1414, the word sequence indicates relative timing relation of thewords within the word sequence.

With reference to FIG. 14B, at block 1416, a plurality of tokens areobtained based on the unstructured natural language information. Atblock 1417, the plurality of tokens represent date and time informationincluded in the unstructured natural language information. At block1418, the plurality of tokens represent entities recognized based on anamed-entity vocabulary.

At block 1420, the input layer is generated using at least one of theword sequence and the plurality of tokens. At block 1421, the inputlayer comprises a plurality of input units. Each of the input unitsrepresents a word or a token. At block 1422, each of the input units hasan 1-of-N encoding. The N represents a total number of words and tokenswithin a vocabulary. The vocabulary is configured to determine whetherevent information is present in the unstructured natural languageinformation.

At block 1424, a hidden layer is generated based on the input layer. Thehidden layer comprises a plurality of current first-context units. Atblock 1425, a current input unit is weighted using a first weightmatrix. At block 1426, a preceding first-context unit is weighted usinga second weight matrix.

With reference to FIG. 14C, at block 1428, a current first-context unitis determined based on the weighting of the current input unit and theweighting of the preceding first-context unit, for example, as describedabove with reference to FIGS. 8, 9A-9C, and 10A-10C. At block 1429, thedetermination of the current first-context unit comprises applying anactivation function. At block 1430, the activation function comprises asigmoid. At block 1431, the activation function comprises a hyperbolictangent. At block 1432, the activate function comprises a rectifiedlinear unit.

At block 1434, the hidden layer further comprises a plurality ofsecond-context units. At block 1435, a current input unit is weightedusing a third weight matrix. At block 1436, a following second-contextunit is weighted using a fourth weight matrix. At block 1437, a currentsecond-context unit is determined based on the weighting of the currentinput unit and the weighting of the following second-context unit, forexample, as described above with reference to FIGS. 8 and 10A-10C.. Atblock 1438, the determination of the current second-context unitcomprises applying an activation function.

At block 1440, an output layer is generated based on the hidden layer.The output layer includes one or more output label units representingthe one or more polarities of at least a portion of the unstructurednatural language information.

With reference to FIG. 14D, at block 1442, the output layer comprises aplurality of output label units. Each output label unit has an 1-of-Kencoding, in which the K represents a number of polarities of apre-determined polarity set. At block 1443, the pre-determined polarityset comprises a proposal, a rejection, an acceptance, and a no-event.

At block 1444, a current first-context unit is weighted using a fifthweight matrix. At block 1446, a current output label unit is determinedbased on the weighting of the current first-context unit, for example,as described above with reference to FIGS. 8, 9A-9C, and 10A-10C. Atblock 1447, determining the current output label unit comprises applyinga softmax activation function.

At block 1448, a current state is obtained based on a currentfirst-context unit and a current second-context unit. At block 1449, thecurrent state is weighted using a sixth weight matrix. At block 1450, acurrent output label unit is determined based on the weighting of thecurrent state, for example, as described above with reference to FIGS.8, 9A-9C, and 10A-10C.. At block 1451, determining the current outputlabel unit comprises applying a softmax activation function.

At block 1454, it is determined whether event information is presentbased on the one or more polarities, for example, as described abovewith reference to FIGS. 8, 9A-9C, and 10A-10C.

With reference to FIG. 14E, to determine whether event information ispresent, at block 1455, it is determined whether the one or morepolarities include at least one of a proposal, a rejection, or anacceptance. At block 1456, in accordance with a determination that theone or more polarities include at least one of a proposal, a rejection,or an acceptance, it is determined a probability associated with the atleast one of a proposal, a rejection, or an acceptance. At block 1457,it is determined whether the probability satisfies a first probabilitythreshold. At block 1458, in accordance with a determination that theprobability satisfies the first probability threshold, it is determinedthat event information is present in the unstructured natural languageinformation.

At block 1460, in accordance with a determination that event informationis present within the unstructured natural language information, it isdetermined whether an agreement on an event is present in theunstructured natural language information, for example, as describedabove with reference to FIGS. 8, 9A-9C, and 10A-10C. To determinewhether an agreement on an event is present, at block 1461, it isdetermined whether the one or more polarities include an acceptance. Atblock 1462, in accordance with a determination that the one or morepolarities include an acceptance, a probability associated with theacceptance is determined. At block 1463, it is determined whether theprobability satisfies a second probability threshold. At block 1464, inaccordance with a determination that probability satisfies the secondprobability threshold, it is determined that an agreement on an event ispresent.

With reference to FIG. 14F, at block 1466, in accordance with adetermination that an agreement on an event is present, the event typeis determined, for example, as described above with reference to FIGS.8, 9A-9C, 10A-10C, 11A-11H, and 12A-12C. At block 1467, determining theevent type is based on a hierarchical classification using a naturallanguage event ontology. At block 1468, the natural language eventontology comprises one or more levels, each level including one or moreevent type nodes. At block 1469, a first-level of the natural languageevent ontology comprises a gathering event type node, an entertainmentevent type node, an appointment event type node, and an arrangementevent type node. At block 1470, the natural language event ontologycomprises a first level and a second level, in which a first-level eventtype node hierarchically relates to one or more second-level event typenodes. At block 1471, at least one event type node is associated with apriority rule. The priority rule indicates a relative priority of oneevent type node with respect to another event type node. At block 1472,at least one event type node of a second or higher level of the naturalevent language ontology is a leaf node. A leaf node does nothierarchically relate to a child node.

At block 1474, a first-level event type associated with the event isdetermined, for example, as described above with reference to FIGS. 8,9A-9C, 10A-10C, and 12A-12C. At block 1475, a first-level event typeoutput of an output layer is determined. The first-level event typeoutput has an 1-of-L encoding, in which the L represents a number offirst-level event type nodes of the natural language event ontology. Atbock 1476, a trailing first-context unit of a hidden layer is weighed.At block 1477, the first-level event type output is determined based onthe weighting of the trailing first-context unit.

With reference to FIG. 14G, at block 1478, a trailing first-context unitand a leading second-context unit is concatenated. At block 1479, theconcatenation of the trailing first-context unit and the leadingsecond-context unit is weighed. At block 1480, the first-level eventtype output is determined based on the weighting of the concatenation ofthe trailing first-context unit and the leading second-context unit. Atblock 1481, determining the first-level event type output comprisesapplying an activation function.

At block 1484, the event type of the event is determined based on thefirst-level event type, for example, as described above with referenceto FIGS. 8, 11A-11H, and 12A-12C. At block 1486, it is determined, foreach second-level event type node, a number of correlations between thesecond-level event type node and the unstructured natural languageinformation. Each second-level event type node is a child node of afirst-level event type node representing the first-level event type. Atblock 1488, a second-level event type is determined based on the numberof correlations of each second-level event type node. At block 1490, itis determined, based on the number of correlations of each second-levelevent type node, the maximum number of correlations. At block 1492, thesecond-level event type is determined based on the maximum number ofcorrelations.

With reference to FIG. 14H, at block 1493, it is determined whether atleast two second-level event type nodes correspond to the maximum numberof correlations, for example, as described above with reference to FIGS.8, 11A-11H, and 12A-12C. At block 1494, in accordance with thedetermination that at least two second-level event type nodes correspondto the maximum number of correlations, the second-level event type isdetermined based on one or more priority rules.

At block 1496, a higher-level event type is hierarchically determinedbased on the second-level event type, for example, as described abovewith reference to FIGS. 8, 11A-11H, and 12A-12C. At block 1498, thehierarchical determination continues until a leaf node is reached. Aleaf node is not hierarchically connected to a child node. At block1500, the hierarchical determination continues until the number ofcorrelations of each higher-level event type node is zero. At block1502, the event type of the event is determined based on thesecond-level event type or a higher level event type. At block 1504, thedetermination of at least one of the first-level event type, thesecond-level event type, a higher-level event type is adjusted based onone or more tokens of the unstructured natural language information.

At block 1505, an event description is provided based on the event type.At block 1506, an event title is provided. The event title includes asubject of a corresponding event type node.

With reference to FIG. 141, at block 1508, a starting time of the eventis provided. At block 1510, an ending time of the event is provided. Atblock 1512, a duration of the event is provided. At block 1514, theevent description of the event is provided based on context information.At block 1516, the context information comprises at least one of alocation, a date, a time, or one or more user preferences.

At block 1518, a calendar entry is generated based on the eventdescription. At block 1520, the calendar entry is displayed. At block1522, one or more affordances are provided. The one or more affordancesenable receiving one or more user inputs with respect to the calendarentry.

At block 1524, one or more user inputs confirming the calendar entry arereceived. At block 1526, the calendar entry is added to the user'scalendar.

With reference to FIG. 14J, at block 1528, one or more user inputsediting the calendar entry are received. At block 1530, the editedcalendar entry is added to the user's calendar. At block 1532, one ormore user inputs denying the calendar entry are received. At block 1534,a dialog is initiated with the user.

At block 1536, a calendar entry is generated based on the eventdescription. At block 1538, the calendar entry is automatically added tothe user's calendar. At block 1540, a booking request is generated basedon the event description. At block 1542, the event description isprovided to one or more applications.

6. Electronic Device

FIG. 15 shows a functional block diagram of an electronic device 1600configured in accordance with the principles of the various describedexamples, including those described with reference to FIGS. 8, 9A-10C,10A-10C, 11A-11H, 12A-12C, 13, and 14A-14J. The functional blocks of thedevice can be optionally implemented by hardware, software, or acombination of hardware and software to carry out the principles of thevarious described examples. It is understood by persons of skill in theart that the functional blocks described in FIG. 15 can be optionallycombined or separated into sub-blocks to implement the principles of thevarious described examples. Therefore, the description herein optionallysupports any possible combination, separation, or further definition ofthe functional blocks described herein.

As shown in FIG. 15, electronic device 1600 can include a microphone1602 and processing unit 1608. In some examples, processing unit 1608includes a receiving unit 1610, an a determining unit 1612, a providingunit 1614, a generating unit 1616, a weighting unit 1618, an applyingunit 1620, an obtaining unit 1622, a concatenating unit 1624, anadjusting unit 1626, a displaying unit 1628, an adding unit 1630, aninitiating unit 1632.

The processing unit 1608 is configured to receive (e.g., with thereceiving unit 1610) unstructured natural language information from atleast one user. In response to receiving the unstructured naturallanguage information, the processing unit 1608 is further configured todetermine (e.g., with the determining unit 1612) whether eventinformation is present in the unstructured natural language information.In accordance with a determination that event information is presentwithin the unstructured natural language information, the processingunit 1608 is further configured to determine (e.g., with the determiningunit 1612) whether an agreement on an event is present in theunstructured natural language information. In accordance with adetermination that an agreement on an event is present, the processingunit 1608 is further configured to determine (e.g., with the determiningunit 1612) determining an event type of the event. The processing unit1608 is further configured to provide (e.g., with the providing unit1614) an event description based on the event type.

In some examples, the unstructured natural language information includesa message.

In some examples, the at least one message includes a text message.

In some examples, the at least one message includes a speech input.

In some examples, the unstructured natural language informationcomprises a plurality of messages.

In some examples, the unstructured natural language informationcomprises at least one electronic mail message.

In some examples, determining whether event information is present inthe unstructured natural language information comprises determining(e.g., with the determining unit 1612) one or more polarities associatedwith the unstructured natural language information, and determining(e.g., with the determining unit 1612) whether event information ispresent based on the one or more polarities.

In some examples, determining one or more polarities associated with theunstructured natural language information includes generating (e.g.,with the generating unit 1616) an input layer based on the unstructurednatural language information. The input layer includes a plurality ofinput units and a plurality of preceding first-context units.Determining one or more polarities associated with the unstructurednatural language information further includes generating (e.g., with thegenerating unit 1616) a hidden layer based on the input layer. Thehidden layer includes a plurality of current first-context units.Determining one or more polarities associated with the unstructurednatural language information further includes generating (e.g., with thegenerating unit 1616) an output layer based on the hidden layer. Theoutput layer includes one or more output label units representing theone or more polarities of at least a portion of the unstructured naturallanguage information.

In some examples, generating the input layer based on the unstructurednatural language information comprises obtaining (e.g., with theobtaining unit 1622) a word sequence based on the unstructured naturallanguage information; obtaining (e.g., with the obtaining unit 1622) aplurality of tokens based on the unstructured natural languageinformation; and generating (e.g., with the generating unit 1616) theinput layer using at least one of the word sequence and the plurality oftokens.

In some examples, the word sequence indicates relative timing relationof the words within the word sequence.

In some examples, the plurality of tokens represent date and timeinformation included in the unstructured natural language information.

In some examples, the plurality of tokens represent entities recognizedbased on a named-entity vocabulary.

In some examples, the input layer comprises a plurality of input units.Each of the input unit represents a word or a token.

In some examples, each of the input unit has an 1-of-N encoding. The Nrepresents a total number of words and tokens within a vocabulary. Thevocabulary is configured to determine (e.g., with the determining unit1612) whether event information is present in the unstructured naturallanguage information.

In some examples, generating the hidden layer based on the input layercomprises weighting (e.g., with the weighting unit 1618) a current inputunit using a first weight matrix; weighting (e.g., with the weightingunit 1618) a preceding first-context unit using a second weight matrix;and determining (e.g., with the determining unit 1612) a currentfirst-context unit based on the weighting of the current input unit andthe weighting of the preceding first-context unit.

In some examples, the determination of the current first-context unitcomprises applying an activation function.

In some examples, the activation function comprises a sigmoid.

In some examples, the activation function comprises a hyperbolictangent.

In some examples, the activate function comprises a rectified linearunit.

In some examples, the hidden layer further comprises a plurality ofsecond-context units.

In some examples, generating the hidden layer based on the input layercomprises weighting (e.g., with the weighting unit 1618) a current inputunit using a third weight matrix; weighting (e.g., with the weightingunit 1618) a following second-context unit using a fourth weight matrix;and determining (e.g., with the determining unit 1612) a currentsecond-context unit based on the weighting of the current input unit andthe weighting of the following second-context unit.

In some examples, the determination of the current second-context unitcomprises applying (e.g., with the applying unit 1620) an activationfunction.

In some examples, the output layer comprises a plurality of output labelunits. Each output label unit has an 1-of-K encoding. The K represents anumber of polarities of a pre-determined polarity set.

In some examples, the pre-determined polarity set comprises a proposal,a rejection, an acceptance, and a no-event.

In some examples generating the output layer based on the hidden layercomprises weighting (e.g., with the weighting unit 1618) a currentfirst-context unit using a fifth weight matrix; and determining (e.g.,with the determining unit 1612) a current output label unit based on theweighting of the current first-context unit.

In some examples, determining the current output label unit comprisesapplying (e.g., with the applying unit 1620) a softmax activationfunction.

In some examples, generating an output layer based on the hidden layerfurther comprises obtaining (e.g., with the obtaining unit 1622) acurrent state based on a current first-context unit and a currentsecond-context unit; weighting (e.g., with the weighting unit 1618) thecurrent state using a sixth weight matrix; and determining (e.g., withthe determining unit 1612) a current output label unit based on theweighting of the current state.

In some examples, determining the current output label unit comprisesapplying (e.g., with the applying unit 1620) a softmax activationfunction.

In some examples, determining whether event information is present basedon the one or more polarities comprises determining (e.g., with thedetermining unit 1612) whether the one or more polarities include atleast one of a proposal, a rejection, or an acceptance. Determiningwhether event information is present based on the one or more polaritiesfurther comprises, in accordance with a determination that the one ormore polarities include at least one of a proposal, a rejection, or anacceptance, determining (e.g., with the determining unit 1612) aprobability associated with the at least one of a proposal, a rejection,or an acceptance; and determining (e.g., with the determining unit 1612)whether the probability satisfies a first probability threshold.Determining whether event information is present based on the one ormore polarities further comprises, in accordance with a determinationthat the probability satisfies the first probability threshold,determining (e.g., with the determining unit 1612) that eventinformation is present in the unstructured natural language information.

In some examples, in accordance with a determination that eventinformation is present within the unstructured natural languageinformation, determining whether an agreement on an event is present inthe unstructured natural language information comprises determining(e.g., with the determining unit 1612) whether the one or morepolarities include an acceptance; in accordance with a determinationthat the one or more polarities include an acceptance, determining(e.g., with the determining unit 1612) a probability associated with theacceptance; determining (e.g., with the determining unit 1612) whetherthe probability satisfies a second probability threshold; and inaccordance with a determination that probability satisfies the secondprobability threshold, determining (e.g., with the determining unit1612) that an agreement on an event is present.

In some examples, determining the event type is based on a hierarchicalclassification using a natural language event ontology.

In some examples, the natural language event ontology comprises one ormore levels. Each level includes one or more event type nodes.

In some examples, a first-level of the natural language event ontologycomprises a gathering event type node, an entertainment event type node,an appointment event type node, and an arrangement event type node.

In some examples, the natural language event ontology comprises a firstlevel and a second level. A first-level event type node hierarchicallyrelates to one or more second-level event type nodes.

In some examples, at least one event type node is associated with apriority rule. The priority rule indicates a relative priority of the atleast one event type node with respect to another event type node.

In some examples, at least one event type node of a second or higherlevel of the natural event language ontology is a leaf node. A leaf nodedoes not hierarchically relate to a child node.

In some examples, determining the event type of the event comprisesdetermining (e.g., with the determining unit 1612) a first-level eventtype associated with the event; and determining (e.g., with thedetermining unit 1612) the event type of the event based on thefirst-level event type.

In some examples, determining the first-level event type comprisesdetermining (e.g., with the determining unit 1612) a first-level eventtype output of an output layer. The first-level event type output has an1-of-L encoding. The L represents a number of first-level event typenodes of the natural language event ontology.

In some examples, determining the first-level event type outputcomprises: weighting (e.g., with the weighting unit 1618) a trailingfirst-context unit of a hidden layer; and determining (e.g., with thedetermining unit 1612) the first-level event type output based on theweighting of the trailing first-context unit.

In some examples, determining the first-level event type outputcomprises concatenating (e.g., with the concatenating unit 1624) atrailing first-context unit and a leading second-context unit; weighting(e.g., with the weighting unit 1618) the concatenation of the trailingfirst-context unit and the leading second-context unit; and determining(e.g., with the determining unit 1612) the first-level event type outputbased on the weighting of the concatenation of the trailingfirst-context unit and the leading second-context unit.

In some examples, determining the first-level event type outputcomprises applying (e.g., with the applying unit 1620) an activationfunction.

In some examples, determining the event type of the event based on thefirst-level event type comprises determining (e.g., with the determiningunit 1612), for each second-level event type node, a number ofcorrelations between the second-level event type node and theunstructured natural language information. Each second-level event typenode is a child node of a first-level event type node representing thefirst-level event type.

In some examples, determining the event type of the event based on thefirst-level event type comprises further determining (e.g., with thedetermining unit 1612) a second-level event type based on the number ofcorrelations of each second-level event type node.

In some examples, determining the second-level event type comprisesdetermining (e.g., with the determining unit 1612), based on the numberof correlations of each second-level event type node, the maximum numberof correlations; and determining (e.g., with the determining unit 1612)the second-level event type based on the maximum number of correlations.

In some examples, the processing unit 1608 is further configured todetermining (e.g., with the determining unit 1612) whether at least twosecond-level event type nodes correspond to the maximum number ofcorrelations; and in accordance with the determination that at least twosecond-level event type nodes correspond to the maximum number ofcorrelations, determine (e.g., with the determining unit 1612) thesecond-level event type based on one or more priority rules.

In some examples, the processing unit 1608 is further configured tohierarchically determine (e.g., with the determining unit 1612) ahigher-level event type based on the second-level event type.

In some examples, the hierarchical determination continues until a leafnode is reached. A leaf node is not hierarchically connected to a childnode.

In some examples, the hierarchical determination continues until thenumber of correlations of each higher-level event type node is zero.

In some examples, the processing unit 1608 is further configured todetermine (e.g., with the determining unit 1612) the event type of theevent based on the second-level event type or a higher level event type.

In some examples, the processing unit 1608 is further configured toadjust (e.g., with the adjusting unit 1626) the determination of atleast one of the first-level event type, the second-level event type, ahigher-level event type based on one or more tokens of the unstructurednatural language information.

In some examples, providing the event description based on the eventtype of the event comprises providing (e.g., with the providing unit1614) an event title. The event title includes a subject of acorresponding event type node.

In some examples, providing the event description based on the eventtype of the event comprises providing (e.g., with the providing unit1614) a starting time of the event.

In some examples, providing the event description based on the eventtype of the event comprises providing (e.g., with the providing unit1614) an ending time of the event.

In some examples, providing the event description based on the eventtype of the event comprises providing (e.g., with the providing unit1614) a duration of the event.

In some examples, the processing unit 1608 is further configured toprovide (e.g., with the providing unit 1614) the event description ofthe event based on context information.

In some examples, the context information comprises at least one of alocation, a date, a time, or one or more user preferences.

In some examples, the processing unit 1608 is further configured togenerate (e.g., with the generating unit 1616) a calendar entry based onthe event description; display (e.g., with the displaying unit 1628) thecalendar entry; and provide (e.g., with the providing unit 1614) one ormore affordances, wherein the one or more affordances enable receivingone or more user inputs with respect to the calendar entry.

In some examples, the processing unit 1608 is further configured toreceive (e.g., with the receiving unit 1610) one or more user inputsconfirming the calendar entry; and add (e.g., with the adding unit 1630)the calendar entry to the user's calendar.

In some examples, the processing unit 1608 is further configured toreceive (e.g., with the receiving unit 1610) one or more user inputsediting the calendar entry; and add (e.g., with the adding unit 1630)the edited calendar entry to the user's calendar.

In some examples, the processing unit 1608 is further configured toreceive (e.g., with the receiving unit 1610) one or more user inputsdenying the calendar entry; and initiate (e.g., with the initiating unit1632) a dialog with the user.

In some examples, the processing unit 1608 is further configured togenerate (e.g., with the generating unit 1616) a calendar entry based onthe event description; and automatically adding (e.g., with the addingunit 1630) the calendar entry to the user's calendar.

In some examples, the processing unit 1608 is further configured togenerate (e.g., with the generating unit 1616) a booking request basedon the event description.

In some examples, the processing unit 1608 is further configured toprovide (e.g., with the providing unit 1614) the event description toone or more applications.

The operation described above with respect to FIG. 16 is, optionally,implemented by the components depicted in FIGS. 1, 2A, 4, 6A-B, 7A-7B,8, or 12A. For example, receiving operation 1610, determining operation1612, and providing operation 1614 are optionally implemented byprocessor(s) 220. It would be clear to a person of ordinary skill in theart how other processes can be implemented based on the componentsdepicted in FIGS. 1, 2A, 4, 6A-B, 7A-7B, 8, or 12A.

It is understood by persons of skill in the art that the functionalblocks described in FIG. 16 are, optionally, combined or separated intosub-blocks to implement the principles of the various describedembodiments. Therefore, the description herein optionally supports anypossible combination or separation or further definition of thefunctional blocks described herein. For example, processing unit 1608can have an associated “controller” unit that is operatively coupledwith processing unit 1608 to enable operation. This controller unit isnot separately illustrated in FIG. 15 but is understood to be within thegrasp of one of ordinary skill in the art who is designing a devicehaving a processing unit 1608, such as device 1600. As another example,one or more units, such as the receiving unit 1610, may be hardwareunits outside of processing unit 1608 in some embodiments. Thedescription herein thus optionally supports combination, separation,and/or further definition of the functional blocks described herein.

For purpose of explanation, the foregoing description has been describedwith reference to specific embodiments. However, the illustrativediscussions above are not intended to be exhaustive or to limit theinvention to the precise forms disclosed. Many modifications andvariations are possible in view of the above teachings. The embodimentswere chosen and described in order to best explain the principles of thetechniques and their practical applications. Others skilled in the artare thereby enabled to best utilize the techniques and variousembodiments with various modifications as are suited to the particularuse contemplated.

Although the disclosure and examples have been fully described withreference to the accompanying drawings, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims.

1. A method, comprising: at an electronic device including at least oneprocessor receiving unstructured natural language information from atleast one user; in response to receiving the unstructured naturallanguage information, determining whether event information is presentin the unstructured natural language information; in accordance with adetermination that event information is present within the unstructurednatural language information, determining whether an agreement on anevent is present in the unstructured natural language information; andin accordance with a determination that an agreement on an event ispresent, determining an event type of the event; and providing an eventdescription based on the event type.
 2. The method of claim 1, whereinthe unstructured natural language information comprises one or moremessages.
 3. The method of claim 1, wherein determining whether eventinformation is present in the unstructured natural language informationcomprises: determining one or more polarities associated with theunstructured natural language information; and determining whether eventinformation is present based on the one or more polarities.
 4. Themethod of claim 3, wherein determining one or more polarities associatedwith the unstructured natural language information comprises: generatingan input layer based on the unstructured natural language information,wherein the input layer comprises a plurality of input units and aplurality of preceding first-context units; generating a hidden layerbased on the input layer, wherein the hidden layer comprises a pluralityof current first-context units; and generating an output layer based onthe hidden layer, wherein the output layer includes one or more outputlabel units representing the one or more polarities of at least aportion of the unstructured natural language information.
 5. The methodof claim 4, wherein generating the input layer based on the unstructurednatural language information comprises: obtaining a word sequence basedon the unstructured natural language information; obtaining a pluralityof tokens based on the unstructured natural language information; andgenerating the input layer using at least one of the word sequence andthe plurality of tokens.
 6. The method of claim 4, wherein generatingthe hidden layer based on the input layer comprises: weighting a currentinput unit using a first weight matrix; weighting a precedingfirst-context unit using a second weight matrix; and determining acurrent first-context unit based on the weighting of the current inputunit and the weighting of the preceding first-context unit.
 7. Themethod of claim 4, wherein the hidden layer further comprises aplurality of second-context units.
 8. The method of claim 7, whereingenerating the hidden layer based on the input layer comprises:weighting a current input unit using a third weight matrix; weighting afollowing second-context unit using a fourth weight matrix; anddetermining a current second-context unit based on the weighting of thecurrent input unit and the weighting of the following second-contextunit.
 9. The method of claim 4, wherein the output layer comprises aplurality of output label units, each output label unit has an 1-of-Kencoding, wherein the K represents a number of polarities of apre-determined polarity set.
 10. The method of claim 9, wherein thepre-determined polarity set comprises a proposal, a rejection, anacceptance, and a no-event.
 11. The method of claim 4, whereingenerating the output layer based on the hidden layer comprises:weighting a current first-context unit using a fifth weight matrix; anddetermining a current output label unit based on the weighting of thecurrent first-context unit.
 12. The method of claim 4, whereingenerating an output layer based on the hidden layer further comprises:obtaining a current state based on a current first-context unit and acurrent second-context unit: weighting the current state using a sixthweight matrix; and determining a current output label unit based on theweighting of the current state.
 13. The method of claim 3, whereindetermining whether event information is present based on the one ormore polarities comprises: determining whether the one or morepolarities include at least one of a proposal, a rejection, or anacceptance; in accordance with a determination that the one or morepolarities include at least one of a proposal, a rejection, or anacceptance, determining a probability associated with the at least oneof a proposal, a rejection, or an acceptance; determining whether theprobability satisfies a first probability threshold; and in accordancewith a determination that the probability satisfies the firstprobability threshold, determining that event information is present inthe unstructured natural language information.
 14. The method of claim3, in accordance with a determination that event information is presentwithin the unstructured natural language information, determiningwhether an agreement on an event is present in the unstructured naturallanguage information comprises: determining whether the one or morepolarities include an acceptance; in accordance with a determinationthat the one or more polarities include an acceptance, determining aprobability associated with the acceptance; determining whether theprobability satisfies a second probability threshold; and in accordancewith a determination that probability satisfies the second probabilitythreshold, determining that an agreement on an event is present.
 15. Themethod of claim 1, wherein determining the event type is based on ahierarchical classification using a natural language event ontology. 16.The method of claim 15, wherein the natural language event ontologycomprises one or more levels, each level including one or more eventtype nodes.
 17. The method of claim 16, wherein at least one event typenode is associated with a priority rule, the priority rule indicating arelative priority of the at least one event type node with respect toanother event type node.
 18. The method of claim 1, wherein determiningthe event type of the event comprises: determining a first-level eventtype associated with the event; and determining the event type of theevent based on the first-level event type.
 19. The method of claim 18,wherein determining the first-level event type comprises determining afirst-level event type output of an output layer, wherein thefirst-level event type output has an 1-of-L encoding, wherein the Lrepresents a number of first-level event type nodes of the naturallanguage event ontology.
 20. The method of claim 19, wherein determiningthe first-level event type output comprises: weighting a trailingfirst-context unit of a hidden layer; and determining the first-levelevent type output based on the weighting of the trailing first-contextunit.
 21. The method of claim 19, wherein determining the first-levelevent type output comprises: concatenating a trailing first-context unitand a leading second-context unit; weighting the concatenation of thetrailing first-context unit and the leading second-context unit; anddetermining the first-level event type output based on the weighting ofthe concatenation of the trailing first-context unit and the leadingsecond-context unit.
 22. The method of claim 19, wherein determining thefirst-level event type output comprises applying an activation function.23. The method of claim 18, wherein determining the event type of theevent based on the first-level event type comprises: determining, foreach second-level event type node, a number of correlations between thesecond-level event type node and the unstructured natural languageinformation, wherein each second-level event type node is a child nodeof a first-level event type node representing the first-level eventtype; and determining a second-level event type based on the number ofcorrelations of each second-level event type node.
 24. The method ofclaim 23, wherein determining the second-level event type comprises:determining, based on the number of correlations of each second-levelevent type node, the maximum number of correlations; and determining thesecond-level event type based on the maximum number of correlations. 25.The method of claim 24, further comprising: determining whether at leasttwo second-level event type nodes correspond to the maximum number ofcorrelations; and in accordance with the determination that at least twosecond-level event type nodes correspond to the maximum number ofcorrelations, determining the second-level event type based on one ormore priority rules.
 26. The method of claim 1, wherein providing theevent description based on the event type of the event comprisesproviding at least one of: an event title, a starting time of the event,an ending time of the event, and a duration of the event, wherein theevent title includes a subject of a corresponding event type node. 27.The method of claim 1, further comprising: generating a calendar entrybased on the event description; displaying the calendar entry; andproviding one or more affordances, wherein the one or more affordancesenable receiving one or more user inputs with respect to the calendarentry.
 28. A non-transitory computer-readable storage medium comprisingone or more programs for execution by one or more processors of anelectronic device, the one or more programs including instructionswhich, when executed by the one or more processors, cause the electronicdevice to perform the method of: receiving unstructured natural languageinformation from at least one user; in response to receiving theunstructured natural language information, determining whether eventinformation is present in the unstructured natural language information;in accordance with a determination that event information is presentwithin the unstructured natural language information, determiningwhether an agreement on an event is present in the unstructured naturallanguage information; and in accordance with a determination that anagreement on an event is present, determining an event type of theevent; and providing an event description based on the event type.
 29. Asystem, comprising: one or more processors; memory; and one or moreprograms stored in memory, the one or more programs includinginstructions for receiving unstructured natural language informationfrom at least one user; in response to receiving the unstructurednatural language information, determining whether event information ispresent in the unstructured natural language information; in accordancewith a determination that event information is present within theunstructured natural language information, determining whether anagreement on an event is present in the unstructured natural languageinformation; and in accordance with a determination that an agreement onan event is present, determining an event type of the event; andproviding an event description based on the event type.